Model Output Research Articles

Multi-regional economic recovery simulation using an Adaptive Regional Input–Output (ARIO) framework

The economic repercussions of natural hazards extend beyond immediate damages, impacting more than the directly affected regions through trading ties and interrupted service provisions. The Adaptive Regional Input–Output (ARIO) model has been developed to assess these indirect impacts by considering changes in productive capacity and demand-driven output adjustments. We extend a recently refined ARIO model (R-ARIO) to conduct a multi-regional multi-sector analysis. This extension accounts for the geographical disparities in direct damages and inter-regional economic interactions. To illustrate the effectiveness of this approach, we present a case study of the 2011 Tohoku earthquake and assess its economic impacts across 13 prefectures in the East-Japan region. The paper further compares the outcomes of single- and multi-regional analyses to showcase the effect of inter-regional trading ties on economic resilience. Additionally, trade-offs involved in selecting the study area boundary for the multi-regional analysis are discussed.

A novel temperature drift compensation method based on LSTM for NMR sensor

Nuclear Magnetic Resonance (NMR) sensors have become one of the best choices for angular velocity sensors in future Inertial Navigation Systems (INS) due to their small size and high precision. The performance of NMR sensors can be affected by temperature changes, resulting in temperature drift. Therefore, temperature compensation is essential. Accurate temperature compensation is challenging due to the lack of direct temperature observations in the core sensitive area (cell) and the complex temperature drift model. Considering the time correlation of temperatures inside and outside the cell, a new temperature drift compensation method based on the Long-Short Term Memory (LSTM) network is proposed. The memory characteristics of the LSTM enable it to fully utilize current and previous data to learn complex models. The main contributions of this study are as follows: (1) the mechanism of temperature drift in NMR sensors was analyzed; (2) a multi-time scale input–output model for temperature changes, temperature change rates, and ambient temperature versus sensor output bias was established; (3) a temperature drift model identification method based on LSTM was designed. Experimental results show that, compared to traditional polynomial fitting and memory-free methods, the proposed method improves temperature compensation accuracy by 26.0% to 47.0%. This method effectively reduces the adverse impact of temperature changes on the NMR sensor.

Carbon emission drivers of China's power sector and its transformation for global decarbonization contribution

To explore the contribution of China's power sector transformation to global CO2 emission reduction and the potential for this sector to achieve peak carbon, we used a hybrid input–output model to explore the direct CO2 emissions of six power sectors classified by generation methods and their impact on CO2 emission by the non-power sector based on the total power sector output (electricity generation). We then used structural decomposition analysis to explore the drivers. From 2012 to 2021, the total indirect CO2 emissions of China's power sector grew from 138.59 Mt to 158.79 Mt (the share in the carbon footprint increased from 3.7% to 3.9%), an increase of 14.6%. Over the period from 2012 to 2021, the power structure effect consistently reduced direct and indirect CO2 emissions. Policy scenario simulations showed that 12 scenarios for 2035 are expected to achieve peak carbon in China's power sector. The transition of China's power sector to cleaner energy will contribute greatly to global CO2 reduction in all sectors. In scenarios APS-DER-LD and APS-DER-HD, where the power structure effect was strongest, China and other countries will simultaneously experience large decreases in carbon emission, with decreases of 43.5 and 36.6%, respectively.

When linear inversion fails: Neural-network optimization for sparse-ray travel-time tomography of a volcanic edifice

In this study, we present an artificial neural network (ANN)-based approach for travel-time tomography of a volcanic edifice under sparse-ray coverage. We employ ray tracing to simulate the propagation of seismic waves through the heterogeneous medium of a volcanic edifice, and an inverse modeling algorithm that uses an ANN to estimate the velocity structure from the “observed” travel-time data. The performance of the approach is evaluated through a 2-dimensional numerical study that simulates i) an active source seismic experiment with a few (explosive) sources placed on one side of the edifice and a dense line of receivers placed on the other side, and ii) earthquakes located inside the edifice with receivers placed on both sides of the edifice. The results are compared with those obtained from conventional damped linear inversion. The average Root Mean Square Error (RMSE) between the input and output models is approximately 0.03 km/s for the ANN inversions, whereas it is about 0.4 km/s for the linear inversions, demonstrating that the ANN-based approach outperforms the classical approach, particularly in situations with sparse ray coverage. Our study emphasizes the advantages of employing a relatively simple ANN architecture in conjunction with second-order optimizers to minimize the loss function. Compared to using first-order optimizers, our ANN architecture shows a ∼25% reduction in RMSE. The ANN-based approach is computationally efficient. We observed that even though the ANN is trained based on completely random velocity models, it is still capable of resolving previously unseen anomalous structures within the edifice with about 5% anomalous discrepancies, making it a potentially valuable tool for the detection of low velocity anomalies related to magmatic intrusions or mush.

Estimating the strength of bi-axially loaded track and channel cold formed composite column using different AI-based symbolic regression techniques

Steel construction is increasingly using thin-walled profiles to achieve lighter, more cost-effective structures. However, analyzing the behavior of these elements becomes very complex due to the combined effects of local buckling in the thin walls and overall global buckling of the entire column. These factors make traditional analytical methods difficult to apply. Hence, in this research work, the strength of bi-axially loaded track and channel cold formed composite column has been estimated by applying three AI-based symbolic regression techniques namely (GP), (EPR) and (GMDH-NN). These techniques were selected because their output models are closed form equations that could be manually used. The methodology began with collecting a 90 records database from previous researches and conducting statistical, correlation and sensitivity analysis, and then the database was used to train and validate the three models. All the models used local and global slenderness ratios (λ, λc, λt) and relative eccentricities (ex/D, ey/B) as inputs and (F/Fy) as output. The performances of the developed models were compared with the predicted capacities from two design codes (AISI and EC3). The results showed that both design codes have prediction error of 33% while the three developed models showed better performance with error percent of 6%, and the (EPR) model is the simplest one. Also, both correlation and sensitivity analysis showed that the global slenderness ratio (λ) has the main influence on the strength, then the relative eccentricities (ex/D, ey/B) and finally the local slenderness ratios (λc, λt).

R2 should not be used to describe behavioral-economic discounting and demand models.

Literature concerning operant behavioral economics shows a strong preference for the coefficient of determination (R2) metric to (a) describe how well an applied model accounts for variance and (b) depict the quality of collected data. Yet R2 is incompatible with nonlinear modeling. In this report, we provide an updated discussion of the concerns with R2. We first review recent articles that have been published in the Journal of the Experimental Analysis of Behavior that employ nonlinear models, noting recent trends in goodness-of-fit reporting, including the continued reliance on R2. We then examine the tendency for these metrics to bias against linear-like patterns via a positive correlation between goodness of fit and the primary outputs of behavioral-economic modeling. Mathematically, R2 is systematically more stringent for lower values for discounting parameters (e.g., k) in discounting studies and lower values for the elasticity parameter (α) in demand analysis. The study results suggest there may be heterogeneity in how this bias emerges in data sets of varied composition and origin. There are limitations when using any goodness-of-fit measure to assess the systematic nature of data in behavioral-economic studies, and to address those we recommend the use of algorithms that test fundamental expectations of the data.

Cross-Linked Starch as Media for Crystal Violet Elimination from Water: Modeling Batch Adsorption with Fuzzy Regression.

A scalable and cost-effective solution for removing pollutants from water is to use biodegradable and eco-friendly sorbents that are readily available such as starch. The current research explored the removal of crystal violet (CV) dye from water using chemically modified potato starch. The adsorbent was prepared by cross-linking potato starch with sodium trimetaphosphate (STMP). The impact of various operating factors including pH, temperature, contact time, initial CV concentration, and adsorbent dosage on the removal of CV were investigated using batch experiments. The adsorption data were analyzed using a fuzzy regression approach, which provided a range-based representation of the model's output. The cross-linked starch adsorbent was mesoporous, with a mean pore diameter of 9.8 nm and a specific surface area of 2.7 m2/g. The adsorption of CV by the STMP cross-linked potato starch was primarily influenced by the adsorbent dosage, followed by the solution pH, temperature, initial CV concentration, and contact time. The fuzzy regression model accurately predicted the independent experimental data of CV removal with an R2 of 0.985, demonstrating its value as a tool for the continuous monitoring of CV removal as well as optimizing water treatment conditions.

Citrus Flavanone Effects on the Nrf2-Keap1/GSK3/NF-κB/NLRP3 Regulation and Corticotroph-Stress Hormone Loop in the Old Pituitary.

Oxidative stress and inflammation are significant causes of aging. At the same time, citrus flavanones, naringenin (NAR), and hesperetin (HES) are bioactives with proven antioxidant and anti-inflammatory properties. Nevertheless, there are still no data about flavanone's influence and its potential effects on the healthy aging process and improving pituitary functioning. Thus, using qPCR, immunoblot, histological techniques, and biochemical assays, our study aimed to elucidate how citrus flavanones (15 mg/kg b.m. per os) affect antioxidant defense, inflammation, and stress hormone output in the old rat model. Our results showed that HES restores the redox environment in the pituitary by down-regulating the nuclear factor erythroid 2-related factor 2 (Nrf2) protein while increasing kelch-like ECH-associated protein 1 (Keap1), thioredoxin reductase (TrxR1), and superoxide dismutase 2 (SOD2) protein expression. Immunofluorescent analysis confirmed Nrf2 and Keap1 down- and up-regulation, respectively. Supplementation with NAR increased Keap1, Trxr1, glutathione peroxidase (Gpx), and glutathione reductase (Gr) mRNA expression. Decreased oxidative stress aligned with NLRP3 decrement after both flavanones and glycogen synthase kinase-3 (GSK3) only after HES. The signal intensity of adrenocorticotropic hormone (ACTH) cells did not change, while corticosterone levels in serum decreased after both flavanones. HES showed higher potential than NAR in affecting a redox environment without increasing the inflammatory response, while a decrease in corticosterone level has a solid link to longevity. Our findings suggest that HES could improve and facilitate redox and inflammatory dysregulation in the rat's old pituitary.

A Bayesian Tensor Decomposition Method for Joint Estimation of Channel and Interference Parameters.

Bayesian tensor decomposition has been widely applied in channel parameter estimations, particularly in cases with the presence of interference. However, the types of interference are not considered in Bayesian tensor decomposition, making it difficult to accurately estimate the interference parameters. In this paper, we present a robust tensor variational method using a CANDECOMP/PARAFAC (CP)-based additive interference model for multiple input-multiple output (MIMO) with orthogonal frequency division multiplexing (OFDM) systems. A more realistic interference model compared to traditional colored noise is considered in terms of co-channel interference (CCI) and front-end interference (FEI). In contrast to conventional algorithms that filter out interference, the proposed method jointly estimates the channel and interference parameters in the time-frequency domain. Simulation results validate the correctness of the proposed method by the evidence lower bound (ELBO) and reveal the fact that the proposed method outperforms traditional information-theoretic methods, tensor decomposition models, and robust model based on CP (RCP) in terms of estimation accuracy. Further, the interference parameter estimation technique has profound implications for anti-interference applications and dynamic spectrum allocation.

The power of mineral: Shock of the global supply chain from resource nationalism

State intervention has long affected the stability of the global mineral and raw material markets. Global energy transition has continued to boost demand for copper, lithium, cobalt, and nickel. It has exacerbated the global prevalence of resource nationalism. Research on resource nationalism has focused on the interaction of economic, political, cultural, and social elements within countries. The lack of understanding of the global economic effects of resource nationalism limits our flexibility in responding to upheavals in the global resource order. We combine time series counterfactual analysis and input–output modeling to advance our understanding of the worldwide supply chain effects of resource nationalism. We analyze the impacts of resource political behavior on global supply chains based on several representative episodes of resource nationalism. While major industrialized countries suffer more absolute welfare losses, formerly developed countries in the Global South face higher price effects. Moreover, nationalist resource shocks are not the only cause of welfare losses in the metals supply chain sector. Still, they are also harming various industries and groups, from food and energy to services. Such differences in price and welfare effects depend on the type of mineral and the resource power of the event-initiating country, on the one hand, and are influenced by the global industrial division of labor, on the other. More losses in developing countries come from the agricultural sector and primary manufacturing than from the severity of the losses suffered by the manufacturing and service sectors in industrial and rich countries. This is a reminder to pay more attention to the unequal distribution of economic pressures across countries and sectors in the global energy transition.

Comparison of the Accuracy of a Deep Learning Method for Lesion Detection in PET/CT and PET/MRI Images.

Develop a universal lesion recognition algorithm for PET/CT and PET/MRI, validate it, and explore factors affecting performance. The 2022 AutoPet Challenge's 1014 PET/CT dataset was used to train the lesion detection model based on 2D and 3D fractional-residual (F-Res) models. To extend this to PET/MRI, a network for converting MR images to synthetic CT (sCT) was developed, using 41 sets of whole-body MR and corresponding CT data. 38 patients' PET/CT and PET/MRI data were used to verify the universal lesion recognition algorithm. Image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Total lesion glycolysis (TLG), metabolic tumor volume (MTV), and lesion count were calculated from the resultant lesion masks. Experienced physicians reviewed and corrected the model's outputs, establishing the ground truth. The performance of the lesion detection deep-learning model on different PET images was assessed by detection accuracy, precision, recall, and dice coefficients. Data with a detection accuracy score (DAS) less than 1 was used for analysis of outliers. Compared to PET/CT, PET/MRI scans had a significantly longer delay time (135 ± 45min vs 61 ± 12min) and lower SNR (6.17 ± 1.11 vs 9.27 ± 2.77). However, CNR values were similar (7.37 ± 5.40 vs 5.86 ± 6.69). PET/MRI detected more lesions (with a mean difference of -3.184). TLG and MTV showed no significant differences between PET/CT and PET/MRI (TLG: 119.18 ± 203.15 vs 123.57 ± 151.58, p = 0.41; MTV: 36.58 ± 57.00 vs 39.16 ± 48.34, p = 0.33). A total of 12 PET/CT and 14 PET/MRI datasets were included in the analysis of outliers. Outlier analysis revealed PET/CT anomalies in intestines, ureters, and muscles, while PET/MRI anomalies were in intestines, testicles, and low tracer uptake regions, with false positives in ureters (PET/CT) and intestines/testicles (PET/MRI). The deep learning lesion detection model performs well with both PET/CT and PET/MRI. SNR, CNR and reconstruction parameters minimally impact recognition accuracy, but delay time post-injection is significant.

Enhancing reliability assessment in distributed generation networks: Incorporating dynamic correlation of wind-solar power output uncertainty

Amidst escalating environmental concerns and energy scarcity, the integration of distributed generation (DG) within distribution networks (DN) has emerged as a pivotal developmental trend. The uncertainty inherent in renewable energy output often disrupts DG networks. Notably, the dynamic correlation between key renewable sources, such as wind and solar energy, significantly influences the reliability analysis of these networks.To comprehensively assess the impact of wind-solar power output uncertainty and its dynamic correlation on DN reliability, this study leverages copula theory to express the dynamic correlation coefficient between wind and solar power. This coefficient is formulated as the dynamic correlation of wind-solar power through copula dynamic correlation coefficient. Employing an auto-regressive moving average (ARMA) model with constraints solved using maximum likelihood kernel (MLK), we construct the wind-solar joint output (WSJO) model. Subsequently, utilizing sequential Monte Carlo simulation (MCS) with the WSJO model, we analyze DN reliability. In case of DN failure, the WSJO model generates random samples of the wind-solar joint output sequence. Subsequent power restoration to governed islands enables the calculation of DN reliability indices. The WSJO model constructed in this study accounts for wind resource output uncertainty and dynamic correlation, aligning more closely with actual distributed generation output and enhancing the accuracy of reliability assessment. Finally, we simulate the improved IEEE-RBTS-BUS6-F4 system to underscore the crucial role of considering wind-solar energy's dynamic correlation in DN reliability assessment.

Enhancing blasting efficiency: A smart predictive model for cost optimization and risk reduction

Mineral extraction involves distinct stages, including drilling, blasting, loading, transporting, and processing minerals at a designated facility. The initial phase is drilling and blasting, crucial for controlled dimensions of crushed stone suitable for the processing plant. Incorrect blasting can lead to unsuitable stone grading and destructive outcomes like ground vibrations, stone projection, air blasts, and recoil. Predicting and optimizing blasting costs (BC) is essential to achieve desired particle size reduction while mitigating adverse blasting consequences. BC varies with rock hardness, blasting techniques, and patterns. This study presents a BC prediction model using data from 6 Iranian limestone mines, employing firefly (FF) and gray wolf optimization (GWO) algorithms. With 146 data points and parameters like hole diameter (D), ANFO (AN), sub-drilling (J), uniaxial compressive strength (σc), burden (B), hole number (N), umolite (EM),spacing (S), specific gravity (γr), stemming (T), hole length (H), rock hardness (HA), and electric detonators (Det), the data was split into 80% for model construction and 20% for validation. Using statistical indicators, the model showed good performance, offering engineers, researchers, and mining professionals high accuracy. The @RISK software conducted sensitivity analysis, revealing T parameter as the most influential input factor, where minor T changes significantly affected BC. Lastly, the @RISK software was employed to conduct a sensitivity analysis on the model's outputs. The analyses demonstrated that, among the input factors, the T parameter had the most pronounced effect on the model's output. Even small changes in the value of T led to considerable fluctuations in the predicted BC.

Multi-dimensional equilibrium optimal allocation of water resources in Beijing-Tianjin-Hebei region through spatial association network analysis

The rapid social and economic development has led to problems such as over-exploitation of water resources, strong demand for physical water resources, and disequilibrium of water resources load. Based on the current situation of the water resources system in Beijing-Tianjin-Hebei region, this study proposed an input–output analysis-assessment-prediction-optimization framework. A water resources dissipation environment extended multi-regional input–output model was established, and the spatial equilibrium of water resources load was evaluated comprehensively. Furthermore, a multi-dimensional spatial equilibrium optimal allocation for water resources was built to identify key nodes and sectors. The primary findings were as follows: (1) Shijiazhuang and Tangshan were the main cities with net virtual water outflow, while Beijing and Baoding were the primary recipients of net virtual water inflow; (2) The overall score for the spatial equilibrium assessment of water resource load was low, with dimension scores predominantly ranging between 0.2 and 0.6; (3) The key transfer nodes were predominantly concentrated on the “outflow from Shijiazhuang” and the “inflow to Cangzhou and Langfang” nodes. This research conducted an in-depth exploration of the direction of water resource transfers, the actual stress conditions, and the spatial equilibrium of water resources within the Beijing-Tianjin-Hebei region. Finally, the future adjustment strategy for water-intensive industries at the urban agglomeration scale was obtained.

Medium-voltage feeder blocks division method considering source-load uncertainty and characteristics complementary clustering

Existing feeder block division methods fail to consider the complementary characteristics and uncertainty between power sources and loads, which result in excessive feeder blocks, low inter-block balance, and significant disparity in net load peak-valley difference. To address these issues, a medium-voltage feeder block division method that considers the uncertainty and complementary characteristics of sources and loads is proposed. Firstly, based on the probability density characteristics of sources and loads, an uncertainty model of DG output and load demand is established. Secondly, considering the constraints of block maximum load rate and feeder non-crossing, a feeder block division model is established. Additionally, a set of center circles is defined, and based on this, an improved K-means clustering algorithm is proposed. The initial clustering centers based on the center circles is set, and the clustering centers based on the arcs of the center circles corrected. And the weighted distances between power sources and clustering centers are calculated. An algorithm flow for improved K-means clustering feeder block division is designed accordingly. Finally, the case studies show that the result of block division is improved.

Backstepping control of hypersonic vehicles under input and output constraints with model uncertainties and disturbances

In this paper, considering the physical restriction of hypersonic vehicle systems in a practical flight environment, the altitude tracking problem is studied with input magnitude, input rate, output constraints, system parameter uncertainties and disturbances, which expands the traditional considerations of control methods in the hypersonic vehicle field. The hyperbolic tangent function is employed to address the system input constraints. Furthermore, the incorporation of barrier Lyapunov function serves to ensure that the system output is limited in the prescribed limitations. In particular, two extended state observers are integrated to eliminate errors from model uncertainties and disturbances. Based on the effective combination of these techniques and auxiliary variable designs, a novel backstepping controller is proposed to simultaneously handle problems of input and output constraints, model uncertainties and disturbances. The closed-loop system stability is demonstrated using the Lyapunov theory. Simulation results show the effectiveness and superiority of the proposed controller compared with the traditional backstepping controller.

RadShap: An Explanation Tool for Highlighting the Contributions of Multiple Regions of Interest to the Prediction of Radiomic Models.

Explaining the decisions made by a radiomic model is of significant interest, as it can provide valuable insights into the information learned by complex models and foster trust in well-performing ones, thereby facilitating their clinical adoption. Promising radiomic approaches that aggregate information from multiple regions within an image currently lack suitable explanation tools that could identify the regions that most significantly influence their decisions. Here we present a model- and modality-agnostic tool (RadShap, https://github.com/ncaptier/radshap), based on Shapley values, that explains the predictions of multiregion radiomic models by highlighting the contribution of each individual region. Methods: The explanation tool leverages Shapley values to distribute the aggregative radiomic model's output among all the regions of interest of an image, highlighting their individual contribution. RadShap was validated using a retrospective cohort of 130 patients with advanced non-small cell lung cancer undergoing first-line immunotherapy. Their baseline PET scans were used to build 1,000 synthetic tasks to evaluate the degree of alignment between the tool's explanations and our data generation process. RadShap's potential was then illustrated through 2 real case studies by aggregating information from all segmented tumors: the prediction of the progression-free survival of the non-small cell lung cancer patients and the classification of the histologic tumor subtype. Results: RadShap demonstrated strong alignment with the ground truth, with a median frequency of 94% for consistently explained predictions in the synthetic tasks. In both real-case studies, the aggregative models yielded superior performance to the single-lesion models (average [±SD] time-dependent area under the receiver operating characteristic curve was 0.66 ± 0.02 for the aggregative survival model vs. 0.55 ± 0.04 for the primary tumor survival model). The tool's explanations provided relevant insights into the behavior of the aggregative models, highlighting that for the classification of the histologic subtype, the aggregative model used information beyond the biopsy site to correctly classify patients who were initially misclassified by a model focusing only on the biopsied tumor. Conclusion: RadShap aligned with ground truth explanations and provided valuable insights into radiomic models' behaviors. It is implemented as a user-friendly Python package with documentation and tutorials, facilitating its smooth integration into radiomic pipelines.

ZCHSound: Open-Source ZJU Paediatric Heart Sound Database With Congenital Heart Disease.

From 2020 to 2022, we collaborated with experienced cardiac surgeons from three general children's hospitals to collect heart sound signals from 1259 participants using electronic stethoscopes. To ensure the accuracy of the labels, the labels for all data were confirmed by two cardiac experts. To establish the baseline of ZCHsound, we extracted 84 features and used machine learning models to evaluate the performance of the classification task. The ZCHSound database was divided into two datasets: one is a high-quality, filtered clean heart sound dataset, and the other is a low-quality, noisy heart sound dataset. In the evaluation of the high-quality dataset, our random forest ensemble model achieved an F1 score of 90.3% in the classification task of normal and pathological heart sounds. This study has successfully established a large-scale, high-quality, rigorously standardized pediatric CHD sound database with precise disease diagnosis. This database not only provides important learning resources for clinical doctors in auscultation knowledge but also offers valuable data support for algorithm engineers in developing intelligent auscultation algorithms.

Artificial Neural Network Model to Predict OBrix and pH of Banana Based on Color Parameters

Artificial neural network (ANN) was used to predict internal quality parameters (oBrix and pH) of lady finger banana. This research consisted of three stages, namely: (1) capturing images of lady finger banana using a computer vision system; (2) measurement of oBrix and pH of the banana; (3) ANN architecture analysis using the Matlab R2019a application. The ANN architectural model consisted of 3 output models, namely: (1) oBrix values; (2) pH value; (3) oBrix and pH values. The ANN architecture analysis was carried out through two phases. Phase I consisted of 45 experimental units and phase II with 35 experimental units. The best ANN architecture to be used as a prediction model for oBrix and pH of golden banana fruit is ANN architecture model 3 with the number of neurons inside the hidden layer = 3; activation function in hidden layer = logsig; activation function inside the output layer = logsig; data transformation range 0 – 1; learning rate value = 0.01; learning algorithm = tradingda; with MSE (mean square error), MAE (mean absolute error) performance and R correlation coefficient from training results of 0.0954; 0.2619 and 0.6538; test results 0.0392; 0.1606 and 0.7000 and validation results 0.0289; 0.1474 and 0.7889. Keywords: Artificial neural networks, Color, Computer vision system, CVS, RGB.

Carbon‐trade nexus: The embodied carbon emission transfer network of China's sustainable development demonstration belt

AbstractUncovering energy‐related carbon emission transfer networks and assessing its structure and efficiency can help reshape the regional carbon‐trade nexus. Combining the social network analysis and interregional input–output model, our study estimates the carbon trade patterns from various perspectives and explores the topological characteristics of energy‐related carbon emission transfer networks within China's green development demonstration belt. Furthermore, carbon pressure index is used to identify key area for the regional carbon reduction. Results indicate that the three reaches are in descending order of embodied carbon emissions whether from the production or consumption perspectives when most of provinces are in the status of “carbon overload.” Furthermore, the total embodied carbon transfers with the large amounts occur at the provinces that are adjacent to each other. In terms of degree centrality, Jiangsu, Anhui, and Zhejiang dominate the carbon emission transfer network within the region, whereas Jiangxi, Sichuan, and Zhejiang serve as the main intermediaries and bridges. As a result, the findings should provide policymakers with new insights into how to formulate more targeted strategies for carbon management in different provinces and sectors.