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The expansion of the digital economy is crucial for halting climate change, as carbon emissions from urban energy use contribute significantly to global warming. This study uses the Difference-in-Differences Model and the Spatial Durbin Model determine whether the digital economy may support the development of reducing carbon emissions and its geographic spillover effects in Chinese cities on the east coast. In addition, it looks more closely at the effects of lowering carbon emissions in space by separating them into direct, indirect, and spatial impact parts. The findings show that (1) from 2012 to 2021, the digital economy favored carbon emission reductions in China’s eastern coastline cities, as supported by the robustness test. (2) The link between digital economy growth and carbon emissions is highly variable, with smart city development and urban agglomeration expansion both cutting city carbon emissions considerably. Successful digital economy strategies can lower CO2 emissions from nearby cities. (3) Eastern coastal cities have a considerable spatial spillover impact, and the digital economy mitigates local energy consumption and carbon emissions while simultaneously enhancing environmental quality in nearby urban areas. This analysis proposes that the peak carbon and carbon neutrality targets can be met by increasing the digital economy and enhancing regional environmental governance cooperation.

Based on the data of existing warehouse supermarkets in Liaoning Province, China, spatial autocorrelation analysis, kernel density analysis, composite correlation coefficient analysis and other methods have been adopted to analyze their spatial-temporal evolution pattern to reflect the general law of the development of China’s existing warehouse supermarkets and fill the gap in this research field. The results show that the spatial distribution of warehouse supermarkets in Liaoning Province is extremely uneven, and areas with high nuclear density are distributed along the “Shenyang-Dalian” line belonging to the aggregation distribution. The Lorentz curve shows a downward trend with a large degree of spatial imbalance, that is, the regional concentration of warehouse supermarkets is high. Through global and local autocorrelation analysis, the regions with similar development levels of warehouse supermarkets in Liaoning Province tend to gather together, and the spatial distribution has a strong correlation. The distribution of warehousing supermarkets in Liaoning Province is affected by traffic location conditions, economic conditions, population quantity and population density, the number of urban functional areas, policy conditions and the role of the government, especially by economic conditions.

China’s steel industry has not yet implemented a carbon tax policy, and its benefits and impacts are still in the theoretical research stage. In addition, enterprises have an insufficient ability to respond to changes in production and sales, which seriously affects the market’s stability. The government should simultaneously start from multiple perspectives, such as energy conservation, emission reduction, dynamic adjustments, and business decisions. Therefore, this research constructs a repeated dynamic game model including carbon tax policy and other mixed reduction policies, and studies the stability and related indicators of the market. The results are as follows: (1) the output adjustment policies that enterprises can implement will show an increasing trend under the single carbon tax policy. (2) The output adjustment policies that enterprises with larger output will also show an increasing trend under the mixed emission reduction policy when emission reduction targets continue to increase. (3) Smaller-output enterprises need to be more cautious in formulating their production plans, and their output adjustment policies will be restricted and affected by more factors. In summary, enterprises should comprehensively consider emission reduction policies, output adjustment policies and other enterprises’ output changes, to ensure that the steel market will not fall into an imbalanced state.

With China’s carbon peak and target proposed and the power system as an important source of carbon emissions, its carbon reduction issues are of great concern. However, the mismatch between power demand, resources, and generation facilities in different regions of China creates enormous carbon reduction pressures, especially in the region covered by China’s Northeast Power Grid (NE Grid). Therefore, in this study, a two-stage power optimization model considering Carbon Capture, Utilization, and Storage (CCUS) was established to optimize the national power generation and transmission in China from 2020 to 2060 first and then to optimize the regional road map of energy structure adjustment for China’s NE grid and its CO2 storage strategy. The first stage optimization results show that the national power interaction in China can make full use of the energy resource advantages of the NE grid, especially Jilin and Mengdong, delivering clean electricity to the North China grid as it reaches its “dual carbon” goal. Additionally, the second-stage optimization results show that the NE grid can make good use of CCUS for its thermal power transition. Annual CO2 storage will peak at 204 Mt in 2050 as thermal power withdraws in the NE grid region, which provides a reference for other regions to realize carbon neutrality of their power system by using CCUS.

Rural tourism is a new point of growth for tourism and the economy in the context of the new normalization of the economy and is of great significance in achieving the complementary coordination and integration of urban and rural areas, promoting rural transformation, and increasing farmers’ incomes. The trends of rural tourism development mechanisms studied on a spatial scale can be used to interpret the sustainable development of rural tourism from different perspectives. Based on the data of key rural tourism villages in China’s Yellow River Basin (hereinafter referred to as the Yellow River Basin), kernel density estimation and spatial hot spot clustering methods were used in the present study to analyze the spatial distribution pattern and sustainable development mechanisms of these villages. The results showed that the spatial distribution of the key villages presents greater concentrations in the west and south than in the east and north, respectively. The spatial distribution of the key villages was found to be primarily affected by factors such as historical culture, transportation locations, economic level, and topography. Finally, the sustainable development mechanisms of rural tourism are proposed, and corresponding suggestions are provided from the perspectives of sustainable livelihoods, operation management, and marketing.

One of the main challenges of employing deep learning models in the field of medicine is a lack of training data due to difficulty in collecting and labeling data, which needs to be performed by experts. To overcome this drawback, transfer learning (TL) has been utilized to solve several medical imaging tasks using pre-trained state-of-the-art models from the ImageNet dataset. However, there are primary divergences in data features, sizes, and task characteristics between the natural image classification and the targeted medical imaging tasks. Therefore, TL can slightly improve performance if the source domain is completely different from the target domain. In this paper, we explore the benefit of TL from the same and different domains of the target tasks. To do so, we designed a deep convolutional neural network (DCNN) model that integrates three ideas including traditional and parallel convolutional layers and residual connections along with global average pooling. We trained the proposed model against several scenarios. We utilized the same and different domain TL with the diabetic foot ulcer (DFU) classification task and with the animal classification task. We have empirically shown that the source of TL from the same domain can significantly improve the performance considering a reduced number of images in the same domain of the target dataset. The proposed model with the DFU dataset achieved F1-score value of 86.6% when trained from scratch, 89.4% with TL from a different domain of the targeted dataset, and 97.6% with TL from the same domain of the targeted dataset.

To study the emission reduction policies’ impact on the production and economic level of the steel industry, this paper constructs a two-stage dynamic game model and analyzes various emission reduction policies’ impact on the steel industry and enterprises. New results are observed in the study: (1) With the increasing emission reduction target (15%−30%) and carbon quota trading price (12.65−137.59 Yuan), social welfare and producer surplus show an increasing trend and emission macro losses show a decreasing trend. (2) Enterprises’ reduction ranges in northwestern and southwestern regions are much higher than that of the other regions; the northeastern enterprise has the smallest reductions range. (3) When the market is balanced (0.8543−0.9320 billion tons), the steel output has decreased and the polarization in various regions has been alleviated to some extent. The model is the abstraction and assumption of reality, which makes the results have some deviations. However, these will provide references to formulate reasonable emissions reduction and production targets. In addition, the government needs to consider the whole and regional balance and carbon trading benchmark value when deciding the implementation of a single or mixed policy. Future research will be more closely linked to national policies and gradually extended to other high-energy industries.

In this paper, a two-stage dynamic game model of China’s iron and steel industry is constructed. Carbon tax levy, product subsidy, carbon capture and sequestration (CCS) and other factors are included in the emission reduction mechanism. The effects of emissions reduction and the economic impact of China’s overall steel industry (and that of its six main regions) are investigated for the first time under different scenarios. As new findings, we report the following: (1) Not all factors declined. The overall social welfare, consumer surplus, output and emissions decrease with a gradual increase in the reduction target, whereas the carbon tax value, unit value of product subsidies and total subsidies show a rising trend; (2) A combination of multiple emissions reduction policies is more effective than a single policy. With the implementation of a combined policy, regional output polarization has eased; (3) Steel output does not exceed 950 million tons, far below the current peak. These results will help the industry to formulate reasonable emissions reduction and output targets. In short, in effort to eliminate industry poverty and to alleviate overcapacity, the industry should not only adopt the various coordinated reduction policies, but also fully consider regional differences and reduction needs.