Carbon Emission Data Research Articles

Online Accounting Method for Carbon Emission of Coal Mines owned by the Coal Industry Group

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.

Fusion model with attention mechanism for carbon-neutral sports competitions

IntroductionIn sports competitions, using energy-saving and emission-reduction measures is an important means to achieve the carbon neutrality goal.MethodsIn this paper, we propose an attention mechanism-based convolutional neural network (CNN) combined with the gated recurrent unit (GRU) for carbon neutral energy saving and emission reduction prediction model in sports competitions, CNN is a feedforward neural network whose input is a two-dimensional matrix. The main feature of CNN is that it can handle multi-channel input data, and the use of GRU can make the model structure simple and largely reduce The use of GRU can make the model structure simple, which largely reduces the hardware computational power and time cost and also better solves the long dependency problem in RNN networks. CNN-GRU extracts the data for features and then optimized by the attention mechanism.ResultsThe model collects real-time carbon emissions data from sports events, including game times, lighting usage, air conditioning emissions and other carbon emissions data, and uses deep learning algorithms to predict and compare carbon emissions from sports competition.DiscussionIn identifying energy saving and emission reduction measures conducive to the realization of the goal of carbon neutral sports events, the model has a certain reference value for realizing energy saving and emission reduction in sports competitions under carbon neutrality goals.

Research on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak: A Case of Henan Province in 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.

Can Chinese cities reach their carbon peaks on time? Scenario analysis based on machine learning and LMDI decomposition

As cities are critical actors in mitigating climate change and achieving the “3060″ target, multi-scenario studies on urban carbon emissions can provide a scientific basis for formulating urban carbon peaking action plans. To remedy the problems of missing regional statistics, inconsistent caliber, and lack of city-scale studies in carbon emission research, this paper uses the sparrow optimization neural network algorithm to fit carbon emission data with nighttime stable light for training. Carbon emission data were obtained for 281 cities in China during 2000–2020. The rates of change of influencing factors are set based on shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) for different periods and different scenarios. The carbon emission and carbon peaking evolution paths of service, industrial and comprehensive cities from 2021 to 2060 are dynamically simulated. The results show that (1) service cities are significantly higher than industrial and comprehensive cities in population, GDP, secondary industry output, and energy consumption. (2) The economic development effect, as the primary driver of carbon emission growth, increases and then decreases in all five categories of cities, with 2010 as the inflection point. Industrial structure improvement has an increasingly strong offsetting effect on carbon emissions and is one of the critical directions for future carbon emission reduction. (3) Service cities such as Beijing and Shanghai are already at the completion stage of urban transformation and are more likely to reach the carbon peak on their own than other types of cities. In the low carbon following scenario, comprehensive cities such as Kaifeng, Rizhao, and Jilin can achieve their carbon peaking targets efficiently. The findings of this paper can provide valid theoretical support for carbon peaking action programs in China and other countries.

How to decouple income growth from household carbon emissions: A perspective based on urban-rural differences in China

As the Chinese living standards rising, the share of household carbon emissions is rapidly increasing. According to Maslow's hierarchy of demand theory, this paper linked household carbon emissions with living standards. Based on urban and rural household carbon emission data from 30 provinces in China and results of group-based trajectory model, we analyzed the convergence of Chinese household carbon emissions. The non-linear driving effect of disposable income on household carbon emissions was confirmed. We draw the following conclusions: First, conditional β and club convergence exist. Second, increasing income is the primary driver of household carbon emissions, and the extent to which income drives household carbon emissions in urban areas decreases as living standards rise. However, the result is not significant in rural. Finally, we confirmed two driving channels of income on household carbon emissions. Increasing income can promote use of clean energy and optimize consumption structure, thus slowing down household carbon emissions. The ultimate solution to controlling household carbon emissions is to develop the economy and decouple income growth from household carbon emissions.

Research on Spatiotemporal Changes in Carbon Footprint and Vegetation Carbon Carrying Capacity in Shanxi Province

The climate and ecological problems caused by excessive carbon dioxide emissions are attracting more and more attention, and the need for carbon reduction has reached a consensus. Carbon peak and carbon neutrality is a solemn commitment made by China to the world and a strategic arrangement to achieve economic and social transformation. This study focused on Shanxi Province, a base of power source and heavy chemical industry in China. Based on energy consumption data, carbon emission data set at the county level, land use data and socioeconomic data, we built a carbon ecological pressure index in order to analyze the spatiotemporal evolution characteristics of the carbon footprint, carbon carrying capacity and carbon ecological security status of each county in Shanxi Province from 2000 to 2020. The results showed that: (1) the total carbon footprint of Shanxi Province increased, and the number of high carbon emission counties showed an increasing trend. The largest part of carbon footprint was coal. (2) The vegetation carbon-carrying capacity showed an increasing trend in general, and forest land was the main contributor to the carbon-carrying capacity. (3) The carbon deficit of Shanxi Province was greater than 0 and behaved as a carbon source. The carbon ecological security decreased from a relatively safe level to a general safe level. (4) The carbon ecological pressure index gradually increased. It was predicted that the carbon ecological security level of each county will remain basically unchanged by 2025 and some will still be at a carbon ecological insecurity level. In general, the carbon ecological pressure index of some counties was still large. It is necessary to strengthen the use of clean energy, optimize the industrial structure and increase the carbon sink of forest land in order to reduce carbon emissions and increase the carbon sink, so as to ensure carbon ecological security and realize the goal of a low-carbon economy.

A compatible carbon efficiency information service framework based on the industrial internet identification

Green and low-carbon is a new development model that seeks balance between environmental sustainability and high economic growth. If explainable and available carbon emission data can be accurately obtained, it will help policy regulators and enterprise managers to more accurately implement this development strategy. A lot of research has been carried out, but it is still a difficult problem that how to accommodate and adapt the complex carbon emission data computing models and factor libraries developed by different regions, different industries and different enterprises. Meanwhile, with the rapid development of the Industrial Internet, it has not only been used for the supply chain optimization and intelligent scheduling of the manufacturing industry, but also been used by more and more industries as an important way of digital transformation. Especially in China, the Industrial Internet identification and resolution system is becoming an important digital infrastructure to uniquely identify objects and share data. Hence, a compatible carbon efficiency information service framework based on the Industrial Internet Identification is proposed in this paper to address the problem of computing and querying multi-source heterogeneous carbon emission data. We have defined a multi cooperation carbon emission data interaction model consisting of three roles and three basic operations. Further, the implementation of the framework includes carbon emission data identification, modeling, calculation, query and sharing. The practice results show that its capability and effectiveness in improving the responsiveness, accuracy, and credibility of compatible carbon efficiency data query and sharing services.

Spatiotemporal evolution and influencing factors of land-use emissions in the Guangdong-Hong Kong-Macao Greater Bay Area using integrated nighttime light datasets

Carbon emissions from land-use and land-cover change (together referred to as ‘land-use emissions’) are an important way to influence the regional carbon balance. However, due to the limitations and complexity of obtaining carbon emissions data at spatial scales, previous studies rarely reveal the long-term evolution characteristics of regional land-use emissions. Therefore, we propose a method to integrate DMSP/OLS and NPP/VIIRS nighttime light images to calculate land-use emissions over a long time series. The accuracy validation results show that the integrated nighttime light images and land-use emissions have a good fit and can accurately assess the long-term evolution of regional carbon emissions. In addition, by combining the Exploratory Spatial Analysis (ESTDA) model and the Vector Autoregressive Regression (VAR) model, we found significant spatial variation in carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), with the two regional emission centres spreading outwards between 1995 and 2020, with an increase in construction land area of 3445 km2, resulting in 257 million tons (Mt) of carbon emissions over the same period. The rapid increase in emissions from carbon sources is not offset by a correspondingly large amount of carbon sinks, resulting in a serious imbalance. Controlling the intensity of land use, optimizing the structure of land use and promoting the transformation of the industrial structure are now the keys to achieving carbon reduction in the GBA. Our study demonstrates the enormous potential of long-time-series nighttime light data in regional carbon emission research.

Carbon productivity and volatility

This study investigates the relationship between firm-level carbon productivity and volatility. With increasing interest in sustainable investing and inclusion of carbon productivity in financial assessments, we examine whether the market considers firms with high carbon productivity as less risky. Using U.S. firm-level carbon emission data, we find that carbon productivity is negatively associated with total and idiosyncratic volatilities. Our main findings hold under propensity score matching and coarsened exact matching. We also show that this relationship is significant when the binding intergovernmental regulations such as the Paris Agreement is active.

Efficient emission reduction through dynamic supply mode selection

Reducing the carbon footprint of global supply chains is a challenge for many companies. Governmental emission regulations are increasingly stringent, and consumers are increasingly environmentally conscious. Companies should therefore integrate carbon emissions in their supply chain decision making. In this paper, we study the inbound supply mode and inventory management decision making for a company that sells an assortment of products. Stochastic demand for each product arrives periodically and unmet demand is backlogged. Each product has two distinct supply modes that differ in terms of their carbon emissions, speed, and costs. The company needs to decide when to ship how much using which supply mode such that total holding, backlog, and procurement costs are minimized while the emissions associated with different supply modes across the assortment remains below a target level. We assume that shipment decisions for each product are governed by a dual-index policy for which we optimize the parameters. We formulate this decision problem as a mixed integer linear program that we solve through Dantzig–Wolfe decomposition. We benchmark our decision model against two state-of-the-art approaches in a large test-bed based on real-life carbon emissions data. Relative to our decision model, the first benchmark lacks the flexibility to dynamically ship products with two supply modes while the second benchmark makes supply mode decisions for each product individually. Our computational experiment shows that our decision model can outperform the first and second benchmark by up to 15 and 40 percent, respectively, for moderate carbon emission reduction targets.

Analysis of Carbon Emissions Embodied in the Provincial Trade of China Based on an Input–Output Model and k-Means Algorithm

National or regional carbon emissions are generally accounted for by the principle of “producer responsibility”, which ignores the embodied carbon emissions implied in product consumption via inter-regional trade. Therefore, it is necessary to include the embodied carbon emissions into the product consumption regions for overall calculation. As an example, this paper analyzes the characteristics of China’s domestic regional carbon flow network based on a multiregional input–output table and carbon emission data, identifying three clusters of carbon emission characteristic regions by k-means—the clustering algorithm of machine learning. The research results show that some provinces—such as Beijing, Zhejiang, and Guangdong—are the net input areas of embodied carbon emissions (“consumers”), consuming products and services produced by “producers” such as Hebei, Shanxi, and Inner Mongolia through trade, implicitly transferring the responsibility for carbon emissions. Accounting for carbon emissions worldwide/countrywide should consider both production responsibility and trade income. Our findings provide a novel national or regional classification approach based on embodied carbon emissions, which calls for an equitable regional distribution system of carbon emission rights. Meantime, inter-regional cooperation is of great significance in achieving carbon neutrality. In particular, the economically developed regions need to offer assistance to improve the energy efficiency or optimize the energy structure in less developed regions, by means of capital investment and technology transformation.

Has China-ASEAN Trade opening increased China’s carbon emissions?

This study uses carbon emission data at the provincial level in China between 1998–2018 and the proportion of the total import and export trade between provinces and the Association of Southeast Asian Nations (ASEAN) in GDP to measure the level of China-ASEAN trade openness. It examines the impact of China-ASEAN trade openness on carbon emissions and its transmission mechanism, and selects the RMB/USD exchange rate as an instrumental variable to address the endogeneity of China-ASEAN trade openness variables. The impact of China-ASEAN trade openness on China’s environment is estimated within a two-stage least squares framework. The results show that trade openness between China and ASEAN positively impacts China’s environment and can facilitate carbon emission reduction. The scale, structural, and technology effects brought by China-ASEAN trade liberalization jointly promote China’s carbon dioxide emission reduction. An inverted “U” relationship is found between economic growth and environmental quality in China, and some provinces and municipalities have now crossed the inflection point of the curve, in which carbon emi-ssions decrease with an increase in per capita wealth.

Accounting CO2 Emissions of the Cement Industry: Based on an Electricity–Carbon Coupling Analysis

Since the cement industry acts as a significant contributor to carbon emissions in China, China’s national emission trading system has announced that it should be included in the system soon. However, current cement carbon accounting methods require high-resolution data from various processes on the production line, making it a cumbersome and costly process. To address this issue, this study explores the feasibility and reliability of using machine learning algorithms to develop electricity–carbon models. These models estimate carbon emissions based solely on electricity data, enabling faster and more cost-effective accounting of carbon in cement production. This study investigates the correlations between electricity data and carbon emissions for a large cement manufacturer in southern China. It compares the performance of models based on the supply of electricity (purchased electricity and waste heat electricity) with those based on the consumption of electricity (electricity used on the grinding machines in the production lines) to identify the key factor for carbon emission calculations. The identified best performing model showed high accuracy, with an R2 of 0.96, an RMSPE of 3.88%, and a MAPE of 2.56%. Based on this, the novel electricity–carbon model has the potential to act as one of the optional methods for carbon emissions accounting in the cement industry and to support carbon emissions data promotion within China’s national emission trading systems.

Accounting for tourism carbon emissions: A consumption stripping perspective based on the tourism satellite account

Limitations in statistical data and differences in accounting methods have hindered the accuracy of tourism carbon emissions accounting. In this research, based on the Tourism Satellite Account (TSA) and underpinned by the logic of “accounting basis–key coefficient–accounting objective,” a comprehensive decomposition accounting method is built from a consumption stripping perspective. First, it classifies the tourism industry by the “sector–industry–product” structure into seven sectors, 13 industries, and 22 characteristic products/services. Next, it strips the actual tourism consumption from the tourism industry by constructing two key coefficients. Finally, it transforms tourism consumption data into carbon emissions data by introducing tourism ecological efficiency. Taking Guangdong province of China as an example, its tourism carbon emissions are calculated from 2010 to 2020 using the proposed method. The results reveal the distribution structure of tourism carbon emissions and confirm the scientific and accurate nature of this accounting method.

Modeling place-based nature-based solutions to promote urban carbon neutrality

Nature-based solutions (NbS) are recognized as widely available and cost-effective mechanisms for sequestering carbon and offsetting carbon emissions. Realistic NbS implementations for carbon neutrality need to be effective at the global level and also appropriate for the socio-economic and physical conditions prevailing at the local level. This paper presents a framework that can help stakeholders identify demands, locations, and types of NbS interventions that could maximize NbS benefits at the local scale. Key processes in the framework include (1) interpolating carbon emissions data at larger spatial scales to high-resolution cells, using land use and socio-economic data; (2) assessing NbS effects on carbon reduction and their location-related suitability, through qualitative literature review, and (3) spatially allocating and coupling multiple NbS interventions to land use cells. The system was tested in Stockholm, Sweden. The findings show that the urban center should be allocated with combinations of improving access to green spaces and streetscapes, while the rural and suburban areas should prioritize preserving and utilizing natural areas. Our proposed method framework can help planners better select target locations for intended risk/hazard-mitigating interventions.

Tracing the CO2 emissions embodied in Chinese mainland's exports with multinational enterprises: From source to sink

The production technologies and carbon intensity of multinational enterprises (MNEs) is significantly different from those of domestic enterprises (DEs). However, there are few studies have paid attention to the enterprise ownerships in tracing the carbon emissions embodied in export (CEEE). This paper constructs a new decomposition framework of total export that considering the MNEs. Based on the OECD AMNE input-output table that distinguishing DEs and MNEs from 2005 to 2016 and the related carbon emissions data, it traces the carbon emissions embodied in Chinese mainland's exports based on the value-added trade. It separates Chinese mainland's domestic carbon emissions export (DCEE) that distinguishing enterprise ownerships, analyzes its changes, and further analyzes the differences of the DCEE between DEs and MNEs from the perspective of industrial distribution and export destinations. The results show that: (1) In addition to 2016, every year more than 10.0% of Chinese mainland's carbon emissions are used to meet the demand of foreign countries or regions, and the MNEs located in Chinese mainland has produced about 6.8%–11.1% of the DCEE. (2) About two-thirds of Chinese mainland's DCEE is produced by Chinese mainland's DEs through intermediate products export, and it is mainly concentrated in the basic metals industry, other non-metallic mineral products industry and chemicals and pharmaceutical products industry. (3) In terms of the DCEE through intermediate products export, the industrial distributions of the DEs and the MNEs are similar, and they both mainly concentrate in the three industries mentioned earlier; While for the final products export, it has much difference that labor-intensive processing trade is mainly produced by the DEs. (4) In terms of the main export destination regions of the DCEE, the DEs and MNEs are very similar both for the intermediate products export and the final products export. And ROW and USA are the biggest export destinations, followed by JPN, KOR, IND, GBR, DEU, etc. The research results of this article have important research significance for promoting the achievement of China's dual carbon goals.

A combined prediction model based on secondary decomposition and intelligence optimization for carbon emission

Accurate prediction of carbon emission is critical for the development of low-carbon economy. However, most carbon emission prediction studies use a single model with low prediction accuracy, and do not consider the instability of carbon emission. Therefore, this paper proposes a combined prediction model of carbon emission. Firstly, the original data is decomposed by singular spectrum decomposition to obtain a limited amount of singular spectrum components. Secondly, high complexity components are secondarily decomposed by variational mode decomposition. Then, chameleon swarm algorithm and carnivorous plant algorithm are used to train the regularization coefficients and kernel parameters of kernel extreme learning machine and least squares support vector machine respectively, and the trained model is used to predict the decomposition components. Finally, induced ordered weighted averaging operator is used to calculate the weight of single model, and error correction is introduced to further promote the prediction accuracy. The carbon emission data of China and the United States is used to make a prediction experiment. The results indicate that the proposed model is superior to other comparative models in different indexes, which provides a new idea for carbon emission prediction.

Analyzing the greenhouse gas emissions in the palm oil supply chain in the VUCA world: A blockchain initiative

AbstractAs the world is becoming more VUCA for business operations, organizations are leveraging the competitive advantage through their supply chain practices. The food supply chain is no exception. Being competitive has become imperative in terms of sustainability and accountability of the business process. Therefore, organizations are adopting blockchain to track and trace the information flow to ensure a better business process. Further, to ensure sustainability, analyzing greenhouse gas (GHG) emissions aids firms in achieving a reasonable improvement in the food supply chain. So, the present study collects carbon emissions data for palm oil production. The study evaluates palm oil production in the Sumatra region, Indonesia (Cradle to Port), through predictive modeling. Here, the palm production process is classified as cultivation, harvesting, and shipping. Then, the study evaluates the GHG emissions from the three phases to understand the carbon footprint in palm oil production. Finally, the study uses blockchain to track and trace the GHG emissions in the palm oil supply chain using Hyperledger Fabric self‐executed smart contracts. The study has developed unique contributions in terms of developing predictive modeling, blockchain architecture, and deep learning in palm production to leverage the competitive advantage of the supply chain in the VUCA world for business excellence in the global market.

Analysis of Carbon Emissions and Emission Reduction from Coal-Fired Power Plants Based on Dual Carbon Targets

The emission of greenhouse gases poses enormous pressure on current carbon emissions and carbon reduction. Accurate quantification of carbon emissions from coal-fired power plants is of great significance for achieving the dual carbon goal. To enable enterprises to better understand their carbon emissions, this study constructs a carbon emission model and carbon emission data accounting model for coal-fired power plants. Case data calculations and a carbon emission reduction analysis were conducted. The experiment showcases that the carbon sensitivity of the inner side of the boiler under control conditions is higher than that of the operating parameters controlled on the inner side of the steam turbine, with a maximum total value of 16.67 g/MJ; the annual average low calorific value of coal remains between 16,000 kJ/kg; the activity level of coal remains between 30,000 TJ; and the oxidation probability of coal char during combustion fluctuates, with a maximum of 99.8%. In the calculation of coal-fired carbon emissions, the fitting difference between the emissions of generator unit 1 and generator unit 2 is maintained within 2%. Overall, the CO2 emissions of power plants involved in the study are generally high. The model built through this study has well analyzed the carbon emissions of power plants. It is of great significance for the actual carbon emission reduction of coal-fired power plants.

Research Progress and Prospect of Tourism Carbon Emissions and Its Uncertainty Analysis Under the Goal of Low-Carbon Scenic Spots

Under the dual carbon goal, China Certified Emissions Reductions (CCER) and the national carbon market have become important means of emission reduction and control. The tourism industry is a strategic pillar industry of China’s national economy, and scenic spots are the main sites of tourism activities. Research on carbon emissions in scenic spots is of great significance for the construction of low-carbon scenic spots and the realization of the dual carbon goal. In this paper, the research on carbon emissions in tourism is reviewed, the current research progress is discussed, and further prospects are made. The research on tourism carbon emissions in China has a good foundation and achieved certain results. However, there are few studies on micro-scales such as scenic spots. The statistical data caliber and measurement methods of carbon emissions are not uniform, and there is a general lack of uncertainty analysis. Future research should focus on building a multi-spatial dimension research system, unifying the statistical caliber and measurement methods of carbon emission data, increasing uncertainty analysis, and ensuring the robustness of research results.