Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm

Carbon emission prediction models: A review

Amidst growing concerns over the greenhouse effect, especially its consequential impacts, establishing effective Carbon Emission Prediction Models (CEPMs) to comprehend and predict CO2 emission trends is imperative for climate change mitigation. A review of 147 Carbon Emission Prediction Model (CEPM) studies revealed three predominant functions-prediction, optimization, and prediction factor selection. Statistical models, comprising 75 instances, were the most prevalent among prediction models, followed by neural network models at 21.8 %. The consistent rise in neural network model usage, particularly feedforward architectures, was observed from 2019 to 2022. A majority of CEPMs incorporated optimized approaches, with 94.4 % utilizing metaheuristic models. Parameter optimization was the primary focus, followed by structure optimization. Prediction factor selection models, employing Grey Relational Analysis (GRA) and Principal Component Analysis (PCA) for statistical and machine learning models, respectively, filtered factors effectively. Scrutinizing accuracy, pre-optimized CEPMs exhibited varied performance, Root Mean Square Error (RMSE) values spanned from 0.112 to 1635 Mt, while post-optimization led to a notable improvement, the minimum RMSE reached 0.0003 Mt, and the maximum was 95.14 Mt. Finally, we summarized the pros and cons of existing models, classified and counted the factors that influence carbon emissions, clarified the research objectives in CEPM and assessed the applied model evaluation methods and the spatial and temporal scales of existing research.

A multi-factor combination prediction model of carbon emissions based on improved CEEMDAN

As the global greenhouse effect intensifies, carbon emissions are gradually becoming a hot topic of discussion. Accurate carbon emissions prediction is an important foundation to realize carbon neutrality and peak carbon dioxide emissions. To accurately predict carbon emissions, a multi-factor combination prediction model based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bidirectional long short-term memory optimized by lemurs optimizer (LOBiLSTM) and least squares support vector machine optimized by lemurs optimizer (LOLSSVM), named ICEEMDAN-LOBiLSTM-LOLSSVM, is proposed. Firstly, the influencing factors of carbon emissions are selected by Spearman correlation coefficient, and carbon emissions are decomposed into intrinsic mode functions (IMFs) by ICEEMDAN. Secondly, the influencing factors and IMFs are input into LOBiLSTM and LOLSSVM respectively for prediction. Then, the point prediction results are obtained by weighting the prediction results of LOBiLSTM and LOLSSVM. Finally, probability density function of point prediction error is calculated by kernel density estimation, and the interval prediction results are calculated according to different confidence intervals. Carbon emissions of China and Germany are selected to verify the superiority of ICEEMDAN-LOBiLSTM-LOLSSVM. The experiment shows that RMSE, MAE, MAPE, and R2 of the proposed model are 0.4468, 0.3612, 0.0120, and 0.9839 respectively for China, which is the best among the nine models, as well as for Germany.

Exploring influential factors of CO2 emissions in China's cities using machine learning techniques

The precise and exhaustive discernment of factors influencing CO₂ emissions underpins the advancement toward sustainable, low-carbon development. Although numerous studies have probed the correlation between predetermined proxy variables and carbon emissions, methodological constraints have often led to an inability to effectively discern carbon emission determinants among numerous potential variables or unravel complex, non-linear relationships, and interaction effects. To redress these research gaps, this research utilized machine learning models to correlate urban CO₂ emissions with socioeconomic indicators. The model outputs were then visualized and interpreted using explainable methods. The findings indicated that the model successfully identified a comprehensive array of dominant influences on urban CO₂ emissions, principally associated with local fiscal policies, land use, energy consumption, industrial development, and urban transportation. The findings further revealed a complex non-linear association between these factors and urban CO₂ emissions; however, the majority of these variables displayed a prevalent propensity to intensify carbon emissions in correspondence with an increase in sample value. Additionally, these factors exhibited a complex interactive influence on urban CO₂ emissions, with distinct pairings producing a suppressive effect exclusively at specific combination of sample values. Consequently, this research posited that a robust correlation between urban socioeconomic development and CO₂ emissions in China remains to be established. Given the varied impacts of these influencing factors across different cities, a differentiated approach to development should be adopted when charting low-carbon trajectories.

Exploring the effect of COVID-19 on airline environmental efficiency through an interval epsilon-based measure model

COVID-19 has dealt an unprecedented blow to the aviation industry since 2020. This paper applies the interval epsilon-based measure (IEBM) model to evaluate the optimal quarterly environmental efficiency of 14 global airlines of passenger and cargo subsystems during 2018-2020. Then, the time series prediction method is applied to forecast the interval data of inputs and outputs from 2021 to 2022. Finally, we can calculate the quarterly efficiency. Thus, the future development trends of airlines can be predicted. The results show that (1) COVID-19 has hit the passenger subsystem harder, while the freight subsystem has become more efficient; (2) the efficiency of the freight subsystem has inevitably declined in the post-epidemic era; and (3) therefore, the airlines will have a "√" shaped recovery curve in the next few years.

Prediction of Carbon Emissions in China’s Power Industry Based on the Mixed-Data Sampling (MIDAS) Regression Model

China is currently the country with the largest carbon emissions in the world, to which, the power industry contributes the greatest share. To reduce carbon emissions, reliable and timely forecasting measures are important and necessary. By using different frequency variables, in this study, we used the mixed-data sampling (MIDAS) regression model to forecast the annual carbon emissions of China’s power industry compared with a benchmark model. It was found that the MIDAS model had a higher prediction accuracy than models such as the autoregressive distributed lag (ARDL) model. Moreover, our results showed that the MIDAS model could conduct timely nowcasting, which is useful when the data have some releasing lag. Through this prediction method, the results also demonstrated that the carbon emissions of the power industry have a significant relationship with GDP and thermal power generation, and that the value of carbon emissions would keep increasing in the years of 2021 and 2022.

Consumption-Based CO2 Emissions on Sustainable Development Goals of SAARC Region

Consumption-based CO2 emission (CBE) accounting shows the possibility of global carbon leakage. Very little attention has been paid to the amount of emissions related to the consumption of products and services and their impact on sustainable development goals (SDGs), especially in the SAARC region. This study used a CBE accounting method to measure the CO2 emissions of five major SAARC member countries. Additionally, a Fully Modified Ordinary Least Square (FMOLS) and a causality model were used to investigate the long-term effects of the CBE and SDG variables between 1972 and 2015. The results showed that household consumption contributed more than 62.39% of CO2 emissions overall in the SAARC region. India had the highest household emissions, up to 37.27%, and Nepal contributed the lowest, up to 0.61%. The total imported emissions were the greatest in India (16.88 Gt CO2) and Bangladesh (15.90 Gt CO2). At the same time, the results for the long-term relationships between the CBEs and SDGs of the SAARC region showed that only the combustible renewables and waste (CRW) variable is significant for most of these countries. The sharing of the responsibility for emissions between suppliers and customers could encourage governments and policymakers to make global climate policy and sustainable development decisions, which are currently stalled by questions over geographical and past emission inequities.

Climate Change-Greenhouse Gas Emissions Analysis and Forecast in Romania

Greenhouse gases (GHG), such as carbon dioxide, methane, nitrous oxide, and other gases, are considered to be the main cause of global climate change, and this problem has received significant global attention. Carbon dioxide has been considered the most significant gas contributing to global climate change. Our paper presents an analysis of the greenhouse gas emissions in Romania along with a forecast for the years to come. For the study, data from the National Institute of Statistics and Eurostat were gathered and used for the analysis in order to present the results. To obtain the results, the data gathered were analyzed using forecasting methods that can be of help in solving some uncertainties that surround the future. The greenhouse gas (GHG) emissions trends in Romania were analyzed both for linear and exponential function methods. The obtained results showed that the linear function analysis of total GHG emissions in Romania had a forecast accuracy higher than the exponential function method. From the analytical methods used we can draw the conclusion that the emissions are on a descending scale and choosing a proper method is important in analyzing data.

Carbon footprint forecasting using time series data mining methods: the case of Turkey

In the globalizing world, many factors such as rapidly increasing population, production and consumption habits, and economic growth cause climate changes. The carbon footprint is a measure of CO₂ emissions released into the atmosphere, which increases day by day, causing glaciers to melt and increase sea level, reduce water resources, and global warming. For Turkey, as a country trying to complete its economic development, signed international agreements such as the Paris Climate Agreement and Kyoto Protocol to reduce the carbon footprint give great importance to the studies estimating carbon footprint and making policies to reduce it. For this reason, in this study it is aimed to estimate the greenhouse gas emissions of Turkey in the year 2030 and to determine its damages to the economy. Time series forecasting algorithm in the WEKA data mining software was used for analysis, and population, gross domestic product, energy production, and energy consumption were used as independent variables. As a result of analysis using data from the years 1990-2017, as long as Turkey continues its course of gradually increasing the amount of current greenhouse gas emissions in the year 2030, 728.3016 metric tons of CO₂ equivalent will be reached. It appears that these estimates remain below the rate of Turkey's commitments at the Paris Climate Agreement that is considered to be promising for Turkey. However, the estimations in other studies should not be ignored; policy makers should determine policies accordingly.

How to Predict Energy Consumption in BRICS Countries?

Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to predict energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the predictive ability of the FGM1,1 with traditional ones, like standard GM1,1 and ARIMA1,1,1 models. Moreover, it illustrates the view of BRICS’s nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM1,1 for a specific range of order parameters and the ARIMA1,1,1 model and the usefulness of both approaches for energy consumption efficient forecasting.

A Novel Comprehensive Benefit Evaluation of IEGES Based on the TOPSIS Optimized by MEE Method

The integrated electricity–gas energy system (IEGES) coordinates the power system and natural gas system through P2G equipment, gas turbines and other coupling components. The IEGES can realize wide-range and long-distance transmission of electricity, heat and natural gas, and truly realize large-scale cross-regional energy supply in space. At present, the theoretical system applicable to the comprehensive benefit evaluation of the IEGES has not been established, and the economic, environmental and social benefits of the system are still at a preliminary study stage. Therefore, the comprehensive benefit evaluation model of the IEGES is constructed, and the integrated benefit evaluation indicator system of the IEGES is designed along the investment and planning, energy supply, equipment operation, power distribution and terminal user. Through the combination of subjective and objective indicator weighting methods, the weights of each indicator are clarified and the matter-element extension theory (MEE) is used to improve the technique for order preference by similarity to ideal solution (TOPSIS), and the comprehensive benefit evaluation model of the IEGES is established. Finally, taking Beijing Yanqing IEGES, Tianjin Eco-city No. 2 Energy Station and Hebei IEGES III as an example, the practicability and effectiveness of the evaluation indicator system and model are verified.

Has air pollution emission level in the Beijing-Tianjin-Hebei region peaked? A panel data analysis

The Beijing-Tianjin-Hebei (BTH) region is one of the important economic centers of China, but it suffers from severe air pollution. Based on the panel pollution-related data of 2013-2017, this research adopted a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) equation to fit the relationship between pollution emission level and its related socio-economic indicators. The pollution emission level of the BTH region was fitted and projected by using the entropy evaluation method to measure the emission levels, the partial least squares algorithm to estimate the STIRPAT equation parameters, and the hybrid trend extrapolation model to forecast the future development of the above socioeconomic indicators. Empirical analysis showed that the fitting curve to air pollution emission level reached the peak in 2015 and then decreased with a fluctuating and slow process. The air pollution emissions in 2025 will decrease to the level of 2007. With regard to the impacts on the change of the air emission pollution level, industrial waste gas emissions play a decisive role. The influence of soot (dust) emissions is considerably smaller but still larger than that of SO₂ emissions. Besides, the slowing down of the economic development in the future will contribute to air quality improvement. However, the rapid growth of population in Hebei and Tianjin would hinder such improvement. Empirical analysis also implied that governments in this region should specially monitor the operation of building material industries to ensure the steady improvement of air quality.

A study on carbon transfer and carbon emission critical paths in China: I-O analysis with multidimensional analytical framework

As environmental issues aggravated heavily, China faces increasing pressure and challenges on carbon emission reduction and distribution. we used non-competitive input-output table (I-O table) combined with the methods of Structural Path Analysis (SPA) and Multidimensional Analytical Framework (MAF), based on the data of China in 2012, to analyze the current situation of inter-sector carbon emission transfer and identify the key sectors and the critical paths from multiple perspectives. Our results show that total fixed capital formation is the main final demand. The electricity, petroleum, and metal smelting are the largest carbon outflow sectors, which emit carbon at the upstream of the path. Construction and other services are the most obvious carbon inflow sectors, which belong to the middle and downstream of the path and lead to indirect carbon emissions through their demands for other sectors. "Metal smelting → Construction → Total fixed capital formation," "Nonmetallic products → Construction → Total fixed capital formation," and "Petroleum → Urban consumption," "Electricity → Urban consumption" are the top four paths with large carbon emission, which deserve attention. Finally, this paper puts forward some policy implications on emission reduction based on the results.

Sustainable Development of New Urbanization from the Perspective of Coordination: A New Complex System of Urbanization‒Technology Innovation and the Atmospheric Environment

Exploring the coordinated development of urbanization (U), technology innovation (T), and the atmospheric environment (A) is an important way to realize the sustainable development of new-type urbanization in China. Compared with existing research, we developed an integrated index system that accurately represents the overall effect of the three subsystems of UTA, and a new weight determination method, the structure entropy weight (SEW), was introduced. Then, we constructed a coordinated development index (CDI) of UTA to measure the level of sustainability of new-type urbanization. This study also analyzed trends observed in UTA for 11 cities in Zhejiang Province of China, using statistical panel data collected from 2006 to 2017. The results showed that: (1) urbanization efficiency, the benefits of technological innovation, and air quality weigh the most in the indicator systems, which indicates that they are key factors in the behavior of UTA. The subsystem scores of the 11 cities show regional differences to some extent. (2) Comparing the coordination level of UTA subsystems, we found that the order is: coordination degree of UT > coordination degree of UA > coordination degree of TA. This suggests that the atmospheric environment system improvement is an important strategic decision for sustainable urbanization in Zhejiang. (3) The UTACDI values of the 11 cities are not high enough, as the coordination is mainly low, basic, or good, while none of the cities reached the stage of excellent coordination. (4) Gray Model (1,1) revealed that the time taking to achieve excellent coordination varies for different cities. Hangzhou and Ningbo were predicted to reach the excellent coordination level in 2018. Other cities are predicted to take 2–4 years to adjust their urbanization strategies enough to be considered to have excellent coordination of their UTA system.

Research on Green Management Effect Evaluation of Power Generation Enterprises in China Based on Dynamic Hesitation and Improved Extreme Learning Machine

Carbon emissions and environmental protection issues have become the pressure from the international community during the current transitional stage of China’s energy transformation. China has set a macro carbon emission target, which will reduce carbon emissions per unit of Gross Domestic Product (GDP) by 40% in 2020 and 60–65% in 2030 than that in 2005. To achieve the emission reduction target, the industrial structure must be adjusted and upgraded. Furthermore, it must start from a high-pollution and high-emission industry. Therefore, it is of practical significance to construct a low-carbon sustainability and green operation benefits of power generation enterprises to save energy and reduce emissions. In this paper, an intuitionistic fuzzy comprehensive analytic hierarchy process based on improved dynamic hesitation degree (D-IFAHP) and an improved extreme learning machine algorithm optimized by RBF kernel function (RELM) are proposed. Firstly, we construct the evaluation indicator system of low-carbon sustainability and green operation benefits of power generation enterprises. Moreover, during the non-dimensional processing, the evaluation index system is determined. Secondly, we apply the evaluation indicator system by an empirical analysis. It is proved that the D-IFAHP evaluation model proposed in this paper has higher accuracy performance. Finally, the RELM is applied to D-IFAHP to construct a combined evaluation model named D-IFAHP-RELM evaluation model. The D-IFAHP evaluation results are used as the input of the training sets of the RELM algorithm, which simplifies the comprehensive evaluation process and can be directly applied to similar projects.

A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

Effectively forecasting energy demand and energy structure helps energy planning departments formulate energy development plans and react to the opportunities and challenges in changing energy demands. In view of the fact that the rolling grey model (RGM) can weaken the randomness of small samples and better present their characteristics, as well as support vector regression (SVR) having good generalization, we propose an ensemble model based on RGM and SVR. Then, the inertia weight of particle swarm optimization (PSO) is adjusted to improve the global search ability of PSO, and the improved PSO algorithm (APSO) is used to assign the adaptive weight to the ensemble model. Finally, in order to solve the problem of accurately predicting the time-series of primary energy consumption, an adaptive inertial weight ensemble model (APSO-RGM-SVR) based on RGM and SVR is constructed. The proposed model can show higher prediction accuracy and better generalization in theory. Experimental results also revealed outperformance of APSO-RGM-SVR compared to single models and unoptimized ensemble models by about 85% and 32%, respectively. In addition, this paper used this new model to forecast China’s primary energy demand and energy structure.

Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China’s electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China’s power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China’s renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China’s renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030.

Analyzing and Forecasting Energy Consumption in China’s Manufacturing Industry and Its Subindustries

In the context of new industrialization, the energy problem being experienced by the manufacturing industry has aroused social concerns. This paper focuses on the energy use of 27 subindustries in China’s manufacturing industry and it develops an energy consumption index for 1994–2015. Subsequently, the method of grey relational analysis is used, with the full period divided according to years in which change points occur. The empirical analysis indicates that the energy consumption indexes generally exhibit a declining trend. Using the grey model (GM (1,1)) to forecast the index indicates a continued downward trend up to 2025 for energy-intensive industries, which is a more optimistic scenario than the trend forecast for the whole manufacturing sector. Thus, these energy-intensive industries do not drag down the performance of the whole manufacturing industry in regard to energy intensity. In future, more attention should be paid to energy-saving efforts by nontraditional high-energy-consuming industries. Although the results show that energy efficiency is improving in China, total annual consumption is rising rapidly. Therefore, the industry needs to continue to strengthen independent innovation and improve the efficiency of new energy use. The Chinese government should formulate feasible long-term plans to encourage enterprises to save energy.