Hulunbeir grassland, as a crucial ecological barrier and energy supply base in northwest China, suffers from a fragile ecological environment. Therefore, it is crucially important for Hulunbeir grassland to achieve the sustainable development of its social economies and ecological environments through the evaluation of its ecological security. This paper introduces the indexes of the ecological pressure index (EPI), ecological footprint diversity index (EFDI), and ecological coordination coefficient (ECC) based on the ecological footprint model. Furthermore, the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was applied to analyze the main driving factors of the change of the ecological footprint. The results showed that: The ecological footprint (EF) per capita of Hulunbeir grassland has nearly doubled in 11 years to 11.04 ha/cap in 2016, while the ecological capacity (EC) per capita was rather low and increased slowly, leading to a continuous increase of per capita ecological deficit (ED) (from 5.7113 ha/cap to 11.0937 ha/cap). Within this, the footprint of fossil energy land and grassland contributed the most to the total EF, and forestland and cropland played the major role in EC. The EPI increased from 0.82 in 2006 to 1.25 in 2016, leading the level of ecological security to increase from level 3 (moderately safe) to level 4 (moderately risky). The indexes of the EFDI and ECC both reached a minimum in 2014 and then began to rise, indicating that Hulunbeir steppe’s ecological environment, as well as its coordination with economy, was considered to be worse in 2014 but then gradually ameliorated. The STIRPAT model indicated that the main factors driving the EF increase were per capita GDP and the proportion of secondary industry, while the decrease of unit GDP energy consumption played an effective role in curbing the continuous growth of the EF. These findings not only have realistic significance in promoting the coordinated development between economy and natural resource utilization under the constraint of fragile environment, but also provide a scientific reference for similar energy-rich ecologically fragile regions.

Ningxia Province is rich in energy but fragile in ecology. How to coordinate sustainable utilization of natural capital and the fragile ecological environment is a significant guarantee for social-economic development. This study uses the improved three-dimensional ecological footprint to characterize the utilization status of natural capital flows and stocks in Ningxia Province from 2004 to 2017. Additionally, the driving factors behind changes in the natural capital stock are revealed by the partial least squares method (PLS). The results are as follows: (1) From 2004 to 2017, ecological footprint increased rapidly in Ningxia Province at an annual rate of 4.52%, resulting in a increase of the ecological deficit from 1.64 to 3.85 gha/cap at an annual rate of 6.8%, among which, Yinchuan city and Shizuishan city had the largest ecological deficit, while Guyuan city basically maintained ecological surplus. The fossil energy land and cropland were the main components of ecological footprint. (2) The consumption of capital stock in Ningxia Province continued to grow at an annual rate of 3.12%, from a value of 2.28 times overusing the existing area in 2004, increasing to 3.41 times in 2017. While the EF size increased slightly with an annual rate of 1.95%. The capital stock consumption was concentrated in Yinchuan and Shizuishan, and the capital flow consumption was concentrated in Wuzhong, Guyuan, and Zhongwei. (3) The capital flows of forest land and built-up land basically meet consumption demand, while the capital stock occupation of grassland, water and fossil energy land was serious. By 2017, the capital flow of cropland could basically satisfy people’s consumption demand. (4) The urbanization rate, GDP, the secondary industry output value and per capita consumption expenditure of urban residents were the main influence factors on the natural capital stock consumption. These findings not only are of real significance in promoting the coordinated development between economy and natural capital utilization in Ningxia Province but also have policy implications in improving the utilization efficiency of natural capital in energy-rich ecologically fragile regions.

Under the background of green development, the spatial structure of urban agglomerations (UA) has an important impact on urban land use efficiency (ULUE), but few studies have explored the impact mechanism between the two. This research explores the impacts of polycentric development on ULUE of UA, using data for 140 cities in China’s top ten key UA covering the period from 2004–2019. The linkage between polycentric development and ULUE is explored by estimating models of determinants of ULUE. This research also examines the mechanism of the polycentric spatial structure of UA on ULUE by using a moderated mediation model. The main findings of the research can be concluded as below. The eastern UAs show a mostly polycentric spatial structure, whereas the central and western UAs show a weak polycentric spatial structure. The polycentric spatial structure of UA has a positive impact on ULUE. An inverted U-shape curve depicts the relationship between the polycentric spatial structure of UA and ULUE. However, the mediating variables, integration of industrial structure and factor mobility have a positive and partially mediating effect between the polycentric spatial structure of UA and ULUE. The infrastructure level has a positive U-shaped regulation effect, in which the impact coefficient of transport infrastructure is more significant. These findings provide empirical evidence for the coordinated development of China’s regional space planning and ULUE.

In order to reduce the depletion of land natural capital and develop economy simultaneously, it is necessary to study how to achieve the strong decoupling relationship between them. However, so far such studies have been relatively limited. Thus, taking the case of Ningxia Hui Autonomous Region, China, this paper firstly analyzes the state of land natural capital utilization in 1999–2017 by using improved ecological footprint. Then, decoupling state is quantified by Tapio decoupling model. Last, major driving factors on the decoupling relationship are explored with combination of LMDI decomposition and Kaya identity equation. Results showed that: (1) Both natural capital flows and stock depletion of cultivated land decrease obviously during the transition to corn-based intensive ecological agriculture. Grassland and water are the most unsustainable development sectors among all land types with their stock depletion intensified. Forest land and construction land could basically meet the consumer demand, but the flow occupancy of construction land is the fastest-growing segment. (2) Decoupling relationship is in an alternating state between weak decoupling and strong decoupling in 1999–2017. Wherein, the cultivated land and forest land showed a preferred decoupling state, followed by grassland, while the water and construction land showed the unfavorable expansive negative decoupling and weak decoupling. (3) Decomposition results show that intensity effect is the major factor that promotes the decoupling while economic effect inhibits the decoupling, but this negative impact is weakening in the process of industrial transformation. The other three factors affect less on the decoupling. This study has a certain reference value to construct an ecological civilization in eco-fragile regions and formulate relevant policies on the increase of land natural capital efficiency.

In the context of international carbon neutrality, energy prices are affected by several nonlinear and nonstationary factors, making it challenging for traditional forecasting models to predict energy prices effectively. The existing literature mainly uses linear models or a combination of multiple models to forecast energy prices. For the nonlinear relationship between variables and the mining of historical data information, the prediction strategy and accuracy of the existing literature need to be improved. Thus, this paper improves the prediction accuracy of energy prices by developing a “decomposition-reconstruction-integration” thinking strategy that affords medium- and short-term energy price prediction based on carbon constraint, eigenvalue transformation and deep learning neural networks. Considering 2011–2020 as the research period, the prices for traditional energy resources and polysilicon in clean photovoltaic energy raw materials are selected as representatives. Based on energy price decomposition using the Singular Spectrum Analysis (SSA) method, and combining it with Learning Vector Quantization (LVQ) cluster technology, the decomposed quantities are aggregated into price sequences with different characteristics. Additionally, the carbon intensity is considered the leading market’s overall constraint, which is input with the processed price data into a Long Short-Term Memory network (LSTM) model for training. Thus, the SSA-LSTM combined forecasting model is developed to predict the energy price under carbon neutrality. Four indices are employed to evaluate the prediction accuracy: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and R-squared. The results highlight the following observations. (1) Using a sequence decomposition clustering strategy significantly improves the model’s prediction accuracy. This strategy enhances predicting the overall trend of the price series and the changes in different periods. For coal price, the RMSE value decreased from 0.135 to 0.098, the MAE value decreased from 0.087 to 0.054, the MAPE value decreased from 0.072 to 0.064, and the R-squared value increased from 0.643 to 0.725. Regarding the polysilicon price, the RMSE value decreased from 0.121 to 0.096, the MAE value decreased from 0.068 to 0.064, the MAPE value decreased from 0.069 to 0.048, and the R-squared value increased from 0.718 to 0.764. (2) The prediction effect is better in the case of carbon constraint. Considering “carbon emission intensity” as the overall constraint of the leading market, it can effectively explore the typical characteristics of energy price information. Four evaluation indicators show that the accuracy of the model prediction can be improved by more than 3%. (3) When the proposed SSA-LSTM model is used to predict both prices, the results show that the evaluation index of the prediction error remained at about 1%, while the model’s accuracy was high. This also proves that the proposed model can predict traditional energy prices and new energy sources such as solar energy.

Understanding the socioecological nexus between urbanization and ecosystem health (EH) is crucial for formulating sustainable development policies. While prior research has focused on this topic, critical gaps persist in characterizing distributional polarization and decomposing inequality drivers within coupled human–environment systems—particularly in China’s Yellow River Basin (YRB), a strategic region undergoing concurrent ecological restoration and urbanization. The integration of the kernel density estimation and Theil index establishes a robust analytical framework to effectively overcome spatial heterogeneity limitations in regional disparity research. Therefore, this study combines the coupling coordination degree (CCD), nonparametric kernel density estimation, and Theil decomposition to examine the complex interactions between urbanization and the ecosystem health index (EHI) across 538 county-level units from the perspective of spatial heterogeneity. The key findings reveal the following: (1) Urbanization exhibited phased enhancement yet maintained elementary developmental stages overall, with a distinct spatial gradient descending from the eastern/central riparian counties to the western hinterlands. (2) The EHI showed a marginal upward trend, yet 80.29% of the counties persisted in the suboptimal ecological health categories (EHI-1 to EHI-3), with gains concentrated in high-vegetation mountainous areas (45.72%) versus declines in economically developed areas. (3) The CCD evolved from a mild imbalance (II-1) to low coordination (III-1) but with significant special differences—the midstream and downstream CCD improved markedly, while the upstream counties remained the weakest. (4) Intragroup disparities, particularly among the counties in the middle reaches, were the primary drivers of CCD disequilibrium across the YRB, contributing 87.9% to the overall inequality. In contrast, the downstream regions exhibited significant improvements in the coordination levels, accompanied by the emergence of distinct “multi-polarization” patterns. The findings provide refined and differentiated decision-making references for effectively narrowing the gap in coordinated development in the YRB.