Information Leakage Prevention and Multi-Scale Feature Modeling for Carbon Emission Time Series Forecasting

The advent of android operating system introduced tools to keep track of users’ information activities and prevent information leakage which bridged the trust between application developers and consumers. Literature shows that several phenomena had been developed to prevent malicious applications from stealing personal sensitive information from smart phones but there is still the need for efficient solutions. This study proposes a conceptual approach for the development of a contentAnalyzer for information leakage detection and prevention on android-based devices. The concept will help to minimize false positives that will in turn lead to increase in code coverage towards detecting the maximum number of data leaks. The proposed concept combines both static and dynamic analysis, and if implemented will improve checking through the codes in the file activities and vulnerabilities that could be a problem. Keywords: Android, ContentAnalyzer, Static Analysis, Dynamic Analysis, Information leakage, Information leakage detection, Information leakage Prevention.

Real-time carbon emission fluctuations characterization and trend prediction from the perspective of complex networks

An online carbon emission accounting method based on B/S structure for coal mines owned by the Coal Industry Group is proposed. Firstly, the carbon emission accounting method for coal mines owned by the Coal Industry Group is expounded, which is the basis of carbon emission accounting for coal mines. Secondly, an online accounting path of carbon emission for coal mines owned by the Coal Industry Group is proposed: The traditional top-down standalone accounting process of "carbon emission data acquisition, carbon emission calculation, carbon emission publication" is replaced by the bottom-up online accounting process of "carbon emission accounting tasks assignment, carbon emission data submission, carbon emission data audit, carbon emission calculation, carbon emission view", and the manual table lookup is replaced by system automatic table lookup to realize online calculation of carbon emissions. Then, taking Sql server as the database management system, ASP, C# as the development language, Dreamweaver, Visual Studio as the development platform, an online carbon emission accounting system based on B/S structure for coal mines owned by the Coal Industry Group is designed, which realizes online carbon emission accounting for coal mines owned by the Coal Industry Group. Finally, the application analysis results show that the method proposed in this paper can not only significantly improve the efficiency and accuracy of carbon emission accounting for coal mines owned by the Coal Industry Group, but also realize online sharing and comparison of accounting results, which is conducive for the Coal Industry Group to implement targeted monitoring and improvement of carbon emissions for coal mines owned by the Coal Industry Group.

Research on small sample carbon emission prediction based on improved TimeGAN: A case study of the Yangtez River Delta urban agglomeration in China.

Government environmental attention and carbon emissions governance: Firm-level evidence from China

This paper presents a prediction model of gated cycle unit (PISC-VMD-GRU) considering the carbon emission from industrial, energy and the total carbon emission and their variation mode decomposition (VMD). First of all, the total carbon emission data is obtained by calculating the carbon emission of industrial, energy, residential, ground traffic, aviation carbon emission data, and VMD method is used to decompose the total carbon emission data. Then, the carbon emission data of industrial, energy, the total, total VMD eigenvector component are taken as the input of GRU, obtaining the prediction of total carbon emission. Finally, the performance were verified on Liaoning and Chinese carbon emission datesets. The experimental results show that compared with GRU, CNN and MLR, on the Liaoning carbon emission dateset, the mean square error (MSE) of PISC-VMD-GRU increased by 95%, 99% and 98.8%, respectively; and on the Chinese carbon emission dateset, the MSE increased by 34.4%, 84.1% and 56.5%, respectively.

China’s carbon emissions are a major global concern. China has proposed a defined “dual-carbon” aim, with the first target being to attain the carbon emissions peak by 2,030. To address this issue, this study provides a two-stage method for forecasting China’s annual carbon emissions, which is paired with pertinent carbon emissions data to predict China’s annual carbon emissions. We discovered the associated aspects affecting China’s carbon emissions through the research of this article, and we predicted the carbon emissions data from 2017 to 2020 using the two-stage technique based on these factors. When compared to the actual data of China’s annual emissions from 2017 to 2020, the prediction intervals from this method encompass the actual data well. This method, on the one hand, identifies the main affecting factors for estimating carbon emissions data, and on the other hand, it validates the method’s performance. It provides support for further policy development and change based on the outcome of this method.

Supply Chain Contracts that Prevent Information Leakage

Facing increasingly severe environmental problems, as the largest developing country, achieving regional carbon emission reduction is the performance of China's fulfillment of the responsibility of a big government and the key to the smooth realization of the global carbon emission reduction goal. Since China's carbon emission data is updated slowly, in order to better formulate the corresponding emission reduction strategy, it is necessary to propose a more accurate carbon emission prediction model on the basis of fully analyzing the characteristics of carbon emissions at the provincial and regional levels. Given this, this paper first calculated the carbon emissions of eight economic regions in China from 2005 to 2019 according to relevant statistical data. Secondly, with the help of kernel density function, Theil index and decoupling index, the dynamic evolution characteristics of regional carbon emissions are discussed. Finally, an improved particle swarm optimization radial basis function (IPSO-RBF) neural network model is established to compare the simulation and prediction models of China's carbon emissions. The results show significant differences in carbon emissions in different regions, and the differences between high-value and low-value areas show an apparent expansion trend. The inter-regional carbon emission difference is the main factor in the overall carbon emission difference. The economic region in the middle Yellow River (ERMRYR) has the most considerable contribution to the national carbon emission difference, and the main contributors affecting the overall carbon emission difference in different regions are different. The number of regions with strong decoupling between carbon emissions and economic development is increasing in time series. The results of the carbon emission prediction model can be seen that IPSO-RBF neural network model optimizes the radial basis function (RBF) neural network, making the prediction result in a minor error and higher accuracy. Therefore, when exploring the path of carbon emission reduction in different regions in the future, the IPSO-RBF neural network model is more suitable for predicting carbon emissions and other relevant indicators, laying a foundation for putting forward more scientific and practical emission reduction plans.

When confronting the dual challenges of rapid urbanization and climate change, although extensive research has investigated the factors influencing urban carbon emissions and the practical strategies regarding urban vibrancy, the unclear mutual nexus between them and the development strategy for collaborative optimization requires further in-depth analysis. This study explores the delicate balance between urban vibrancy and low-carbon sustainability within the confines of Beijing’s Fifth Ring Road. By integrating OpenStreetMap, land use, population, and buildings’ carbon emission data, we have developed a reproducible method to estimate total carbon emissions and emission intensity. Furthermore, we have introduced vibrancy index data to distinguish the vibrancy evaluation of residential and non-residential land and applied cross-combinational classification technology to dissect the spatial correlation between urban carbon emissions and urban vibrancy. The results reveal that the four combination typologies show more significant differences and regularity in residential land. Based on the discovery of spatial correlation, this study puts forward corresponding development strategy suggestions for each of these four typologies based on the geographical location and requirements of urban development policies. In conclusion, our study highlights the importance of integrating carbon emissions and urban vibrancy comprehensively in sustainable urban planning and proposes that various land use combinations need targeted development strategies to achieve this goal, which need to consider population, energy, service facilities, and other diverse aspects.

Incidents involving data breaches are ever-present in the media since several years. In order to overcome this threat, organizations apply enterprise content-aware data leakage prevention (DLP) solutions to monitor and control data access and usage. However, this paper argues that current solutions are not able to reliably protect information assets. The analyses of data breaches reported in 2014 reveal a significant number of data leakage incidents that are not within the focus of the DLP solutions. Furthermore, these analyses indicate that the classification of the provided data breach records is not qualified for detailed investigations. Therefore, advanced criteria for characterizing data leakage incidents are introduced, and the reported records are extended. The resulting analyses illustrate that DLP and information leakage prevention (ILP) demand various information security (IS) measures to be established in order to reduce the risk of technologically based data breaches. Furthermore, the effectiveness of DLP and information leakage prevention (ILP) measures is significantly influenced by non-technological aspects, such as the human factor. Therefore, this paper presents a concept for establishing DLP and ILP within the scope of IS.

The carbon market is a key tool for China to meet its emission reduction targets, but it is still in the early stages of development. More evidence is needed to assess its effectiveness in reducing carbon emissions. This paper establishes an evolutionary game model to analyze the interaction between the government and enterprises and applies the Gradient Boosting Decision Tree (GBDT) algorithm to identify carbon emission reduction effects of the carbon market based on carbon emission data from 2000 to 2019. The theoretical model reveals that the construction of China’s carbon market needs to go through three stages: stages of lack of enthusiasm from both the government and enterprises, government dominance, and market dominance. The empirical results show that the carbon market has a significant carbon emission reduction effect, which affects regional carbon emissions through technological innovation, fiscal, and digitalization effects. Further analysis indicates that the maturity of the carbon market and the readjustment of industrial structure contribute to carbon emission reduction effects. Although carbon emission reduction effects are not achieved by reducing labor employment, a resource curse effect may still emerge. This study deepens the understanding of China’s carbon market construction and offers valuable insights for policy practices aimed at high-quality development.

It is an indisputable fact that carbon emissions lead to global warming. Finding a rapid and accurate method for estimating carbon emissions is the prerequisite for making real-time emission reduction measures. In this paper, an estimation method for quick inversion of provincial-level carbon emissions in China is proposed by using night-time light data. This method was based on the corrected night-time light image and combined with the statistical data of the built-up area to extract the total night light value (TDN) in the built-up areas of 30 provinces (Municipalities directly under the Central Government and autonomous regions were collectively referred to as provinces; Tibet, Hong Kong, Macao and Taiwan were not involved here) in Chinese mainland from 1997 to 2017. The regression equation was established by using the TDN of the built-up areas in each province from 1997 to 2014 and the provincial-level carbon emission data released by CEADs (China emission accounts and datasets) in the same period, and then the TDN values from 2015 to 2017 were used as the independent variable to estimate the carbon emission of each province according to the established regression equation. Finally, we used the entropy method and carbon emission allocation model to distribute China’s national-level carbon emission data released by the international authoritative databases to each province and compared them with the provincial-level carbon emissions estimated by the above regression equations from 2015 to 2017. The results show that: (1) There was a significant linear relationship between the established carbon emission estimation models in all provinces, with R2 values greater than 0.8 except Beijing, Hainan and Shanxi. (2) Comparing the difference between the estimated carbon emissions and the carbon emissions allocated to provinces by the database, except for Shandong, Shanxi, Inner Mongolia and Shaanxi provinces, the errors of the other provinces were relatively small, RMSE and MAE were less than 260mt, and the MAPE of most provinces were less than 50%, indicating that the estimation models have high goodness-of-fit and accuracy. (3)The provincial-level carbon emissions allocated by the four international databases from 2015 to 2017 and the carbon emissions estimated by the model were plotted separately, and it is found that the corresponding scatter points of most provinces were distributed near the 1:1 line, which once again showed that the carbon emissions inverted based on night-time light data were close to the carbon emissions allocated to the provinces by each database, especially the provincial-level carbon emissions from CEADs database. The above results demonstrate that this method can provide a faster and more accurate estimation of provincial-level carbon emissions for China.

Buildings are considered to have significant emission reduction potential. Residential building carbon emissions, as the most significant type of building-related carbon emissions, represent a crucial factor in achieving both carbon peak and carbon neutrality targets for China. Based on carbon emission data from Henan Province, a large province located in central China, between 2010 and 2020, this study employed the Kaya-LMDI decomposition method to analyze seven driving factors of carbon emission evolution, encompassing energy, population, and income, and assessed the historical reduction in CO2 emissions from residential buildings. Then, by integrating Kaya identity static analysis with Monte Carlo dynamic simulation, various scenarios were established to infer the future evolution trend, peak time, and potential for carbon emission reduction in residential buildings. The analysis results are as follows: (1) The carbon emissions of residential buildings in Henan exhibited a rising trend from 2010 to 2020, albeit with a decelerating growth rate. (2) Per capita household disposable income is the main driving factor for the increase in carbon emissions, but the household housing purchase index inhibits most of the growth of carbon emissions for the residential buildings in Henan, with the total carbon emission reduction of residential buildings reaches 106.42 million tons of CO2 during the research period. (3) During the period from 2020 to 2050, residential buildings in Henan Province will exhibit an “inverted U-shaped” trend in carbon emissions under the three static scenarios. The base scenario predicts that carbon emissions will reach their peak of 131.66 million tons in 2036, while the low-carbon scenario forecasts a peak of 998.8 million tons in 2030 and the high-carbon scenario projects a peak of 138.65 million tonnes in 2041. (4) Under the dynamic simulation scenario, it is anticipated that residential buildings in Henan Province will reach their carbon peak in 2036 ± 3 years, with a corresponding carbon emission of 155.34 million tons. This study can serve as a valuable reference for the future development of low-carbon pathways within the building sector.

The increase in carbon emissions has had great negative impacts on the healthy developments of the human environment and economic society. However, it is unclear how specific socio-economic factors are driving carbon emissions. Based on the multiscale geographically weighted regression (MGWR) model, this paper analyzes the impact mechanism of China's carbon emission data during 2010-2017. The results show that (1) during the study period, China's carbon emissions have obvious positive correlations in the spatial distribution, and the spatial autocorrelation of carbon emissions on the time scale has a further strengthening trend. (2) Compared with the results of the geographically weighted regression (GWR) model, the MGWR model is more robust, and the results are more realistic and reliable. The impacts of energy intensity, proportion of green coverage in built-up areas, and industrial structure on provincial carbon emissions are close to the global scale, and their spatial heterogeneity is weak. Other factors have spatially heterogeneous impacts on carbon emissions with different scale effects. (3) Except for proportion of green coverage in built-up areas, the industrial structure and trade openness have insignificant impacts on carbon emissions, but other variables have significant impacts. The total population, urbanization rate, energy intensity, and energy structure have positive impacts on carbon emissions, while the GDP per capita and foreign direct investment have negative impacts on it. This study shows that the main socio-economic factors have different degrees of impacts on carbon emissions with different scale, and we can refer to it to formulate more scientific measures to reduce carbon emissions.