Carbon-Aware Dynamic Task Scheduling in Hierarchical Cloud–Edge Systems for IoT Devices

The characteristics of common prosperity include harmonious relationships between humans and the environment, as well as sustainable economic and social growth. The process of achieving common prosperity will necessarily have an impact on carbon emissions. In this article, panel statistics collected from 30 Chinese provinces and cities between the years 2006 and 2020 are utilized to assess the level of common prosperity and the intensity of carbon emissions in China. Then the SDM model is applied to explore the effects of the common prosperity level on the intensity of carbon emissions. The findings reveal that: (i) The common prosperity level in China has shown an increasing tendency. Between 2006 and 2020, the mean level of common prosperity increased from 0.254 to 0.486. From the regional perspective, eastern China has seen greater levels of common prosperity than central China, while central China has experienced greater levels of common prosperity than western China; regional disparities in the degree of common prosperity are substantial among Chinese provinces from 2006 to 2020; the common prosperity level is relatively high in economically developed provinces and relatively low in economically backward provinces. (ii) China's carbon emission intensity shows a continuous downward tendency. The annual average intensity of China's carbon emissions decreased from 4.458 in 2006 to 2.234 in 2020. From the regional perspective, the three main regions' carbon emission intensity likewise exhibits a decline in tendency between 2006 and 2020; still, western China continues to have the greatest carbon emission intensity, following central China, while eastern China has the smallest; however, certain provinces, notably Inner Mongolia and Shanxi, continue to have high carbon emission intensity. (iii) China's common prosperity level and carbon emission intensity both exhibit positive spatial autocorrelation at a 1% significant level under the adjacency matrix. The spatial agglomeration effect is significant, and adjacent provinces can affect each other. (iv) The SDM (Spatial Durbin Model) model test with fixed effects finds that the increase in the level of common prosperity suppresses the intensity of carbon emissions in the local area and neighboring regions. (v) The mediating effects model indicates that the process of common prosperity suppresses carbon emission intensity through high-quality economic development, narrowing the income disparity, and the development of a sharing economy.

To accurately measure the carbon emission intensity of tourism, a comprehensive measurement method is proposed in this study. This method combines statistical data and standard deviation ellipse analysis, which can reflect the actual situation of carbon emission in tourism more comprehensively. The spatial autocorrelation of regional tourism is obtained by global Moran's I index and local Moran's I index, and the spatial and temporal evolution characteristics of tourism carbon emission intensity are extracted by standard deviation ellipse analysis. By calculating the consumption stripping coefficient, carbon emission intensity and total carbon emission of tourism, the carbon emission intensity of tourism is calculated. China is divided into eastern, central and western regions, and the carbon emission level and intensity in the region are calculated. The results show that: (1) from 2012 to 2021, the carbon emissions of tourism in various regions generally showed an increasing trend, but the carbon emissions in the eastern region were the highest. (2) From 2018 to 2021, the carbon emission intensity of tourism in different regions is basically the same, and the research period shows a certain downward trend. (3) The accuracy of calculating the carbon emission intensity of tourism in each region obtained by this method can reach 86.5%.

Cloud computing is mathematical process that provides more power and flexibility in computing infrastructure. Cloud computing provides internet services using a network of remote services. The core service for any environment is the best business plan that supports better quality of service (QoS). Task scheduling in the cloud is a key issue that needs to be addressed to improve system performance and high customer satisfaction. The task scheduling affects the exact time of operation and the cost of using the system. In this paper, we propose a capuchin search algorithm based task scheduling (CSTS) in cloud computing environment. In CSTS method, first we introduce an improved cuttlefish optimization (ICFO) algorithm for task clustering which groups user task into two set as normal and emergency task. Then, we develop a modified capuchin search (MCS) algorithm for priority based optimal task scheduling which minimize makespan and improve resource utilization. Finally, the simulation results of proposed CSTS method is compared with the existing state-of-art methods in terms of makespan, execution time, deadline violation rate and resource utilization.

The increasing popularity of Internet of Things (IoT) applications highlights the demand for task scheduling in the cloud–fog scenarios, where low latency, short makespan, and minimal energy use are of the utmost concern. Although prior optimization methods solved the problems, limitations remain in convergence speed and overall scheduling performance. We present an Enhanced Locust Swarm Optimization Algorithm (ELSOA) for scheduling IoT tasks across fog nodes and cloud servers. ELSOA integrates Opposition-Based Learning (OBL) and chaotic sine mapping to improve the balance between exploration and exploitation, accelerating convergence and avoiding local optima. Experimental results using both simulated and real-world datasets (GoCJ) demonstrate that ELSOA achieves an average reduction of 19.3% in makespan and 17.7% in energy consumption compared to state-of-the-art methods. These findings confirm that ELSOA offers a scalable and effective solution for dynamic IoT task scheduling in large-scale fog–cloud environments.

With the proposal of “Carbon Peak and Carbon Neutrality” goals, China is facing a more serious carbon emissions reduction situation, and how the booming digital economy effectively helps China’s carbon emissions reduction is one of the most urgent things that should be solved. To study the impact of the digital economy on carbon emission intensity, this paper is based on the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2011 to 2021, and applies the double-fixed effect model and the threshold effect model to study the impact of the digital economy on carbon emission intensity and the mechanism of its action, as well as to analyze the mechanism of the digital economy’s action on carbon emission intensity from the perspective of technological innovation. The results of the study show that: i) The digital economy can reduce the intensity of regional carbon emissions; ii) The carbon emission reduction effect of the digital economy is non-linear, and its carbon emission reduction effect gradually increases with the level of development of the digital economy; iii) In addition to the direct impact of the digital economy on carbon emission intensity, it also has an indirect impact on carbon emissions through technological innovation; iv) There is regional heterogeneity in the carbon emission reduction effect of the digital economy, and the carbon emission reduction effect is more significant in the central and western parts of the country and regions with a high level of human capital development. Based on the conclusions obtained, this paper suggests: i) The rational integration of the digital economy and regional development should be strengthened; ii) Strong provinces in the digital economy should be encouraged to help weaker provinces, to narrow the “digital divide” between provinces; iii) Differentiated development strategies should be formulated in accordance with local conditions, to give full play to the optimal effect of the digital economy in carbon emission reduction.

The digital economy (DE) is an essential transmitter of CO2 within the economic system, significantly impacting carbon emissions and high-quality development. The Yellow River Basin (YRB) and the Yangtze River Economic Belt (YREB), China’s two most important economic regions, are critical strategically for achieving the dual carbon target. This paper uses panel data from the YRB and YREB in China from 2011 to 2021 and adopts fixed and mediating effects to explore the internal impact mechanisms and spatial heterogeneity of the DE and carbon emission intensity (CEI) in the context of new quality productivity (NQP). The research results indicate that (1) the DE can significantly reduce CEI. A 1% increase in the DE reduces CEI by 0.1536% in the YRB and 0.0643% in the YREB, respectively. (2) The DE can affect CEI in the YRB and the YREB through mechanisms such as the economic development level, industrial structure advancement and rationalization, energy structure, and level of technological progress, with industrial structure advancement having the highest impact. (3) The YRB has a lower level of DE development and a higher overall CEI than the YREB. (4) A 1% increase in the DE leads to regional CEI variations: in the upstream of the YRB and YREB, CEI decreases by 0.1424% and 0.1956%, respectively, whereas in the midstream of the YRB, it decreases by 0.1298%, and in the downstream of the YREB, it increases by 0.0707%. We propose accelerating the development of the DE and constructing a green and modernized industrial system to achieve carbon reduction and emission mitigation goals.

It is of great significance to study the regional differences and temporal and spatial evolution of China’s carbon emission intensity under the carbon emissions trading mechanism, and to explore the potential for regional emission reduction. This paper uses the Theil index and Moran index to analyze the regional differences and temporal and spatial evolution trend of carbon emission intensity in China from 2010 to 2019, further constructs the emission reduction effect standard of carbon emissions trading mechanisms, discusses the emission reduction effect of the trading mechanisms, and measures the regional emission reduction potential according to the environmental learning curve. The results showed that: (1) China’s overall carbon emissions continued to increase, but the carbon emission intensity showed an overall decreasing trend. There are strong regional differences in China’s carbon emission intensity. The carbon emission intensity in the western region is higher, and the overall regional difference is decreasing year by year. (2) China’s carbon emissions trading mechanism has a significant reduction effect, but the total quota slack of the Tianjin, Beijing, and Chongqing carbon emissions trading pilot markets is loose. (3) Shanghai, Shanxi, Jiangxi, Guizhou, Inner Mongolia, and Beijing are high-efficiency carbon emission reduction provinces (more than 35%), and Fujian and Xinjiang are low-efficiency carbon emission reduction provinces (less than 15%). It is necessary to further develop the demonstration effect of high emission reduction potential areas and increase the emission reduction efforts in low emission reduction potential areas.

This article studies the energy carbon emission (ECE) evaluation problem for enterprises, and designs ECE-codes to conduct multi-factor grading evaluation of the ECE. The ECE evaluation results of enterprises need to be displayed to non-professionals such as consumers. Therefore, in addition to being able to characterize the ECE level, the ECE evaluation results of enterprise should be intuitive and easy to understand. For this purpose, the ECE-codes are designed, including the horizontal identification code (HIC), the efficiency identification code (EIC), and the neutralization identification code (NIC). Define carbon emission intensity (CEI) as the ECEs required to produce an unit of profit. The HIC is formed by dividing the CEI of enterprises into three levels, and is used to grade an enterprise's carbon emission level in the entire industrial sector. The EIC is formed through five-level evaluation of CEI in sector (CEIS), which is used to measure the CEI level of an enterprise in its sector. The CEIS is the ratio of CEI to the average value of the sector CEI. NIC is used to display an enterprise's carbon neutrality process, which is the ratio of carbon neutrality to total carbon emissions. To achieve effective grading of CEI and CEIS, a Gaussian mixture model (GMM) is established to describe the distribution of the CEI and CEIS, and the model parameters are identified by the expectation maximization (EM) algorithm. Then, the grading thresholds can be obtained according to the GMM probability density function with given percentage parameters. The effectiveness of the ECE-codes based grading evaluation method is verified by applying this method to the carbon emission evaluation of the registered enterprises in Huzhou, china.

How does the concentration of spatial allocation of urban construction land across cities affect carbon emission intensity in China?

With the replication and promotion of the experience of pilot reform and innovation, as well as the continuous deepening of institutional openness, exploring green and low-carbon models has become an important measure for the country to promote the high-quality development of free trade pilot zones. This study focused on the core task of institutional innovation in free trade pilot zones, measured and quantified the level of green investment liberalization and green trade facilitation in each region, and explored the carbon emission dual control effect of free trade pilot zones using cutting-edge methods, such as group period average processing effect estimation （CATT）, imputation, and stacked DID. The research conclusions were as follows： ① The phased establishment of free trade pilot zones reduced the total carbon emissions by 27.40% and the carbon emission intensity by 14.30%. ② The pilot free trade zone achieved dual carbon emission control through the spillover effect of green innovation from foreign investment, the direct effect of green innovation from local enterprises, and the indirect effect of green innovation from digital trade. The comprehensive implementation of the negative list of market access, the signing and implementation of free trade agreements between China and other countries, and the increase of international green trade barriers have all had a significant positive regulatory effect on the dual control of carbon emissions in free trade pilot zones. The dual control effect of carbon emissions in the free trade pilot zone exhibited significant spatial spillover characteristics, showing maximum efficiency within a radius of 550 km, resulting in a reduction of 18.30% in the total carbon emissions of surrounding cities and a decrease of 10.10% in carbon emission intensity. Heterogeneity analysis showed that regions with higher levels of institutional innovation and government function transformation had a 13.10% and 13.70% higher total carbon emission control effect compared to that of regions with lower levels and a 8.40% and 3.40% higher carbon emission intensity control effect, respectively. The total carbon emission control effect of cities along the "Belt and Road" was 9.60% higher than that of cities not along the line, and the carbon emission intensity was 8.80% higher. The total carbon emission control effect of cities that had opened the China Europe freight train was 16.80% higher than that of cities that had not opened it, and the carbon emission intensity was 7.50% higher. In terms of aligning with international economic and trade rules and achieving high-quality development, the free trade pilot zone should focus on the green and low-carbon qualification review of newly registered foreign-funded enterprises entering and the first attempt to break through technical barriers to green trade, to help carbon emission dual control process of China with high-level openness and coordination.

With regard to the aims of achieving the “Dual Carbon” goal and addressing the significant greenhouse gas emissions caused by urban expansion, there has been a growing emphasis on spatial research and the prediction of urban carbon emissions. This article examines land use data from 2000 to 2020 and combines Grid and the PLUS model to predict carbon emissions in 2030 through a multi-scenario simulation. The research findings indicate the following: (1) Between 2000 and 2020, construction land increased by 95.83%, with carbon emissions also increasing. (2) By 2030, for the NDS (natural development scenario), carbon emissions are expected to peak at 6012.87 × 104 t. Regarding the ratio obtained through the EDS (economic development scenario), construction land is projected to grow to 3990.72 km2, with expected emissions of 6863.29 × 104 t. For the LCS (low-carbon scenario), the “carbon peak” is expected to be reached before 2030. (3) The intensity of carbon emissions decreases as the city size increases. (4) The shift of the center of carbon emission intensity and the center of construction land all indicate movement towards the southeast. Studying the trends of regional land use change and the patterns of land use carbon emissions is beneficial for optimizing the land use structure, thereby enabling us to achieve low-carbon emission reductions and sustainable urban development.

Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency

Within the context of China’s commitment to carbon reduction goals, particularly in urban areas, addressing carbon emissions stemming from residents’ travel activities assumes paramount significance. Drawing upon established theoretical frameworks, this study advances several hypotheses delineating the determinants of low-carbon behaviors among urban residents. It analyzes panel data from 30 provincial capitals in China using a time–individual dual fixed effects model. This study empirically scrutinizes the posited theoretical model, aiming to elucidate the factors shaping urban residents’ low-carbon behavioral patterns and provide a decision-making basis for low-carbon construction and management of urban space. The findings underscore several notable associations. The disposable income, population density, and urban built-up areas exhibit significant positive correlations with carbon emissions among residents. Conversely, the urban gross domestic product (GDP) displays a significant negative correlation with carbon emissions. Furthermore, a positive correlation is discerned between the expanse of green spaces and the per capita carbon emissions intensity, while the availability of subway systems exhibits a negative correlation with both the per capita public green space area and the carbon emissions intensity. Notably, the configuration intensity of urban bus systems manifests an inverted U-shaped relationship with residents’ carbon emissions intensity. Specifically, within a certain threshold, an escalation in the bus equipment intensity coincides with heightened carbon emission intensity; however, beyond this threshold, a notable reduction in the per capita carbon emissions intensity ensues. Additionally, a U-shaped relationship is observed between the number of urban parks and residents’ carbon emissions intensity, indicating that an increase in parks may not necessarily contribute to carbon reduction efforts. Moreover, a discernible synergy is observed among various factors influencing carbon reduction efforts. These factors encompass residents’ education levels and disposable incomes, the presence of subway and regular public transportation systems, urban land utilization scales, economic development levels, green space provisions, public transportation infrastructure, population densities, and land equilibrium. This interplay underscores the interconnectedness and interdependence of diverse variables in shaping strategies for mitigating carbon emissions within urban contexts.

Due to the imbalance of technological level and industrial structure in regional economic development, the same carbon source can bring differentiated carbon emission levels in different regions, thus making the carbon emission show significant regional differences. In order to explore the regional differences in China's provincial carbon emission intensity and the effect of relevant influencing factors, this paper combines EKC model and STIRPAT model to conduct research. Using carbon emission intensity and other influencing factors of China's 30 provinces ranging from 2005 to 2017 to construct a panel data, the authors use exploratory spatial data analysis and Spatial Durbin Model to study the spatial effect of carbon emission intensity in China's provincial regions and the impact of different development factors on carbon emission intensity. The results show that from 2005 to 2017, China's carbon emission intensity gradually declined from east to west and from south to north. The inter-provincial carbon emission intensity of China presents an agglomeration effect in space, and the agglomeration effect gradually weakens with time. In addition, reducing energy intensity can reduce carbon emission intensity to a large extent. By optimizing industrial structure, increasing the degree of foreign trade and promoting financial development, carbon emission intensity can also be inhibited. Therefore, reducing the energy intensity of various industries and establishing inter-regional carbon emission cooperation mechanism will be effective to control the carbon emission intensity.

The tourism economy is regarded as an effective way to realize regional sustainable development. Hence, it is of great significance to explore whether and how tourism economy can alleviate regional carbon emission intensity. To this end, a structural equation model (SEM) reflecting the multiple pathways of the carbon emission reduction effect of tourism economy was constructed based on 92 tourism-dependent cities in China, and the existence and formation mechanism of the carbon emission reduction effect of tourism economy were empirically tested. The main findings are as follows: (1) The tourism economy has a significant carbon emission reduction effect in China. Although the direct impact of tourism economy on carbon emission intensity is significantly positive, the indirect impact is significantly negative and stronger than the direct impact. (2) The carbon emission reduction effect of tourism economy presents multiple pathways characteristics. There are single intermediary pathways such as Tourism Economy → Environmental Regulation → Carbon Emission Intensity, Tourism Economy → Opening-Up → Carbon Emission Intensity, and dual intermediary pathways such as Tourism Economy → Opening-Up → Industrial Development → Carbon Emission Intensity, Tourism Economy → Opening-Up → Innovation Capacity → Carbon Emission Intensity. (3) The formation mechanism of the carbon emission reduction effect of tourism economy presents obvious spatial heterogeneity.