Forecasting CO2 Emissions Research Articles

Forecasting CO2 Emissions in India: A Time Series Analysis Using ARIMA

Forecasting CO2 Emissions in India: A Time Series Analysis Using ARIMA

Achieving China's CO2 reduction targets: Insights from a hybrid PPA-PPR forecasting model

China is the largest carbon dioxide (CO2) emitter and has formulated CO2 emission peak and carbon-neutral plans. Studies on CO2 emission volume and CO2 emission intensity (CEI) indicate a growing interest in related fields. The purpose of this research is to improve the performance and reliability of the model for forecasting CO2 emissions and judging whether to achieve China's CO2 reduction targets under business-as-usual scenarios. We originally applied a novel hybrid model combining the projection pursuit regression (PPR) model and parasitism-predation optimization algorithm (PPA) (PPA-PPR) to forecast the CO2 emissions from 2022 to 2035, with the time series' CO2 emissions data from 1965 to 2017 and compare with various machine learnings. From the studied results, we can conclude that the hybrid PPA-PPR has better stability and accuracy than BPNN, RF, SVM, GM, TDGM, and LSTM models. The mean absolute percentage error (MAPE) of the verification set data is only 1.19%, and the MAPE of forecasting set data from 2018 to 2021 is 1.44, which obviously outperforms the BPNN, RF, SVM, GM, TDGM, and LSTM models. The second finding is that if China continues to develop in its present trend, it can't implement the CO2 emission peak and reduction CEI target by over 65% by 2030. The limitation of this research is that we don't decompose the time series data into intrinsic modes to study the possibility of improving the model performances.

Mining interpretable fuzzy If-Then linguistic rules from energy and economic data to forecast CO[formula omitted] emissions of regions in China

Forecasting CO2 emission is the one of most important issues for the “30⋅60” CO2 emission target in China. Due to unbalanced socio-economic developments of regions in China, exactly forecasting CO2 emissions of provinces depend on their energy consumptions and economic developments. In the paper, a novel method based on K-means clustering method and computing with words is proposed to forecast CO2 emissions of 30 provinces, which is consisted by (1) K-means clustering method is used to respectively cluster energy consumption and economic datasets of provinces and the interpretable fuzzy If-Then linguistic rules of CO2 emissions are mined from the clusters; (2) computing with words method is utilized to transform fuzzy If-Then linguistic rules into fuzzy If-Then rules with membership functions on the universe of discourse; (3) a fuzzy inference method is adopted to forecast CO2 emissions of 30 provinces. To show the usefulness and effectiveness of the novel method, energy consumptions and economic datasets of 30 provinces from 1997 to 2021 are employed to forecast CO2 emissions, metrics of MAE, MAPE, RMSE and the out-of-sample Roos2 are utilized to evaluate CO2 emission forecasting of 30 provinces, means of them reach 13.304, 15.279, 0.081 and 0.965. By comparative analysis for forecasting CO2 emissions of 30 provinces, means of MAE, MAPE, RMSE and the out-of-sample Roos2 by the novel method are more than SVM, ANFIS and MLR methods. In addition, four kinds of mechanisms influencing CO2 are discovered and analyzed by the fuzzy If-Then linguistic rules of 30 provinces, which can help to improve energy consumption and sustainable development of 30 provinces in China.

Forecasting CO2 Emissions from Libya’s Transport Sector

This paper presents an innovative approach to forecast carbon dioxide (CO2) emissions from the transport sector in Libya. The method combines machine learning algorithms with historical data and future estimates. The research built a model that took into account factors such as population growth, rates of car ownership, patterns of fuel consumption and government regulations in order to provide an accurate forecast of carbon dioxide (CO2) emissions over the next decade based on the Global Change Assessment Model (GCAM). The authors used a variety of statistical time series models to forecast future CO2 emissions from Libya's transportation sector. These models included the exponential smoothing model (ESM) and the autoregressive integrated moving average (ARIMA). The ARIMA model outperformed the ESM model, achieving an R2 of 0.931 and a root mean square error (RMSE) of 1.040 Mt CO2. The results of the study found that CO2 emissions from Libya's transport sector could increase by 27.98% and 57.99% in 2030 and 2050, respectively. The study proposed six transportation theories to reduce CO2 emissions from Africa's and Libya's transport sectors. The identified factors encompass price systems, land use planning, eco-driving, electric automobiles, bicycle infrastructure, and telecommuting. The authors also examined the needs to reduce CO2 emissions from Libya’s transport sector in order to meet the International Energy Agency’s ambitious targets for reducing CO2 emissions from the global transport sector. These needs arise due to increasing urbanization, population growth, underinvestment in public transportation infrastructure, and the increasing incidence and severity of heat waves. Additionally, hypothetical scenarios are presented to demonstrate the importance of further reducing CO2 emissions from these sectors to match the projections of global change assessment models.

Forecasting CO2 emissions of fuel vehicles for an ecological world using ensemble learning, machine learning, and deep learning models.

The continuous increase in carbon dioxide (CO2) emissions from fuel vehicles generates a greenhouse effect in the atmosphere, which has a negative impact on global warming and climate change and raises serious concerns about environmental sustainability. Therefore, research on estimating and reducing vehicle CO2 emissions is crucial in promoting environmental sustainability and reducing greenhouse gas emissions in the atmosphere. This study performed a comparative regression analysis using 18 different regression algorithms based on machine learning, ensemble learning, and deep learning paradigms to evaluate and predict CO2 emissions from fuel vehicles. The performance of each algorithm was evaluated using metrics including R2, Adjusted R2, root mean square error (RMSE), and runtime. The findings revealed that ensemble learning methods have higher prediction accuracy and lower error rates. Ensemble learning algorithms that included Extreme Gradient Boosting (XGB), Random Forest, and Light Gradient-Boosting Machine (LGBM) demonstrated high R2 and low RMSE values. As a result, these ensemble learning-based algorithms were discovered to be the most effective methods of predicting CO2 emissions. Although deep learning models with complex structures, such as the convolutional neural network (CNN), deep neural network (DNN) and gated recurrent unit (GRU), achieved high R2 values, it was discovered that they take longer to train and require more computational resources. The methodology and findings of our research provide a number of important implications for the different stakeholders striving for environmental sustainability and an ecological world.

Sustainability with Limited Data: A Novel Predictive Analytics Approach for Forecasting CO2 Emissions

AbstractUnforeseen events (e.g., COVID-19, the Russia-Ukraine conflict) create significant challenges for accurately predicting CO2 emissions in the airline industry. These events severely disrupt air travel by grounding planes and creating unpredictable, ad hoc flight schedules. This leads to many missing data points and data quality issues in the emission datasets, hampering accurate prediction. To address this issue, we develop a predictive analytics method to forecast CO2 emissions using a unique dataset of monthly emissions from 29,707 aircraft. Our approach outperforms prominent machine learning techniques in both accuracy and computational time. This paper contributes to theoretical knowledge in three ways: 1) advancing predictive analytics theory, 2) illustrating the organisational benefits of using analytics for decision-making, and 3) contributing to the growing focus on aviation in information systems literature. From a practical standpoint, our industry partner adopted our forecasting approach under an evaluation licence into their client-facing CO2 emissions platform.

Forecasting CO2 Emissions in Malaysia Through ARIMA Modelling: Implications for Environmental Policy

Forecasting CO2 Emissions in Malaysia Through ARIMA Modelling: Implications for Environmental Policy

Modeling CO2 Emission Forecasting in Energy Consumption of the Industrial Building Sector under Sustainability Policy in Thailand: Enhancing the LISREL-LGM Model

This research aims to study and develop a model to demonstrate the causal relationships of factors used to forecast CO2 emissions from energy consumption in the industrial building sector and to make predictions for the next 10 years (2024–2033). This aligns with Thailand’s goals for sustainability development, as outlined in the green economy objectives. The research employs a quantitative research approach, utilizing Linear Structural Relationships based on a Latent Growth Model (LISREL-LGM model) which is a valuable tool for efficient country management towards predefined green economy objectives by 2033. The research findings reveal continuous significant growth in the past economic sector (1990–2023), leading to subsequent growth in the social sector. Simultaneously, this growth has had a continuous detrimental impact on the environment, primarily attributed to the economic growth in the industrial building sector. Consequently, the research indicates that maintaining current policies would result in CO2 emissions from energy consumption in the industrial building sector exceeding the carrying capacity. Specifically, the growth rate (2033/2024) would increase by 28.59%, resulting in a surpassing emission of 70.73 Mt CO2 Eq. (2024–2033), exceeding the designated carrying capacity of 60.5 Mt CO2 Eq. (2024–2033). Therefore, the research proposes strategies for country management to achieve sustainability, suggesting the implementation of new scenario policies in the industrial building sector. This course of action would lead to a reduction in CO2 emissions (2024–2033) from energy consumption in the industrial building sector to 58.27 Mt CO2 Eq., demonstrating a decreasing growth rate below the carrying capacity. This underscores the efficacy and appropriateness of the LISREL-LGM model employed in this research for guiding decision making towards green economy objectives in the future.

Modeling and forecasting carbon dioxide emission in Pakistan using a hybrid combination of regression and time series models

Carbon dioxide (CO2) emissions continue to rise globally despite efforts to combat climate change. Energy industry emissions are a pressing global issue, causing devastating impacts. Hence, it is vital to accurately and efficiently forecast CO2 emissions. Thus, this study comprehensively analyzes forecasting CO2 emissions by comparing various hybrid combinations of regression and time series methods to explore the CO2 emissions in Pakistan. First, divide the yearly time series of CO2 emissions into the long-run curve trend series and the residual subseries. The long-run curve trend subseries is modeled using parametric and nonparametric regression methods, while various standard time series models are used to forecast the residual subseries. However, the forecasts of each subseries will be combined to obtain the final forecast of CO2 emissions. This work used four different accuracy mean errors, a statistical test, and a graphical analysis as performance measures to evaluate the proposed hybrid forecasting technique. The findings confirmed that the proposed hybrid combination forecasting technique is highly accurate and efficient in forecasting CO2 emissions. Likewise, according to the proposed final optimal hybrid combination forecasting model, Pakistan's per capita CO2 emissions will be 1.130215 metric tons in 2030. Pakistan's escalating emission trend signals that creative solutions must be implemented to curb it. Thus, the government must price carbon footprints, regulate electricity from zero-carbon sources, reduce population, encourage afforestation in densely populated areas, adopt clean technology, and fund research.

A Novel Geophysical Method to Monitor Ultra-Shallow Reservoirs: Mapping of Soil Moisture Content in Subsiding Peatlands to Forecast Drought Effects and CO2 Emissions

A Novel Geophysical Method to Monitor Ultra-Shallow Reservoirs: Mapping of Soil Moisture Content in Subsiding Peatlands to Forecast Drought Effects and CO2 Emissions

The research on a novel multivariate grey model and its application in carbon dioxide emissions prediction.

Accurate small-sample prediction is an urgent, very difficult, and challenging task due to the quality of data storage restricted in most realistic situations, especially in developing countries. The grey model performs well in small-sample prediction. Therefore, a novel multivariate grey model is proposed in this study, called FBNGM (1, N, r), with afractional order operator, which can increase the impact of new information and background value coefficient to achieve high prediction accuracy. The utilization of an intelligence optimization algorithm to tune the parameters of the multivariate grey model is an improvement over the conventional method, as it leads to superior accuracy. This study conducts two sets of numerical experiments on CO2 emissions to evaluate the effectiveness of the proposed FBNGM (1, N, r) model. The FBNGM (1, N, r) model has been shown through experiments to effectively leverage all available data and avoid the problem of overfitting. Moreover, it can not only obtain higher prediction accuracy than comparison models but also further confirm the indispensable importance of various influencing factors in CO2 emissions prediction. Additionally, the proposed FBNGM (1, N, r) model is employed to forecast CO2 emissions in the future, which can be taken as a reference for relevant departments to formulate policies.

Enhancing Carbon Capture and Storage Deployment in the EU: A Sectoral Analysis of a Ton-Based Incentive Strategy

The EU considers carbon capture and storage (CCS) technology as an option for achieving climate goals, but its cost remains appreciable. Therefore, the purpose of this research was to investigate the implementation of a ton-based incentive system for CCS in the EU using Croatia as an example based on an analysis of the existing legislative framework in the EU and relevant tax credit provisions in the USA. A novel methodology for the design of the incentive system is presented in the form of partial allocation of the state’s auction revenues from the EU emissions trading system (ETS) into the CCS fund for five years. The CCS fund assets then incentivize the capture site for 10 years. The incentives are determined for each emitter in cement, electricity, paper and pulp, glass, oil refining, and petrochemical sectors based on varying European Union allowance (EUA) prices, CCS fund sizes, and CO2 emission scenarios. In addition to designing the methodology, a novel method for forecasting CO2 emissions is applied using geometric Brownian motion. The calculated incentives are categorized as underperforming, optimal, or overperforming, with upper and lower limits set to 80 and 10 EUR/t. The results are optimistic, since all sectors can be efficiently incentivized within the defined boundaries, meaning that the incentive system can be applied to all member states. The contracting of the incentives is proposed through carbon contracts for difference to avoid irregularities. Also, regulatory amendments are proposed so that emitters with emissions higher than 100 kt would have to consider CCS. Finally, the contributions are presented by proving the feasibility of the incentive system together with demonstrating its applicability to all member states.

Modeling and forecasting CO2 emissions in China and its regions using a novel ARIMA-LSTM model

Since China joined the WTO, its economy has experienced rapidly growth, resulting in significantly increase in fossil fuel consumption and corresponding rise in CO2 emissions. Currently, China is the world's largest emitter of CO2, the regional distribution is also extremely uneven. so, it is important to identify the factors influence CO2 emissions in the three regions and predict future trends based on these factors. This paper proposes 14 carbon emission factors and uses the random forest feature ranking algorithm to rank the importance of these factors in three regions. The main factors affecting CO2 emissions in each region are identified. Additionally, an ARIMA + LSTM carbon emission predict model based on the inverse error combination method is developed to address the linear and nonlinear relationships of carbon emission data. The findings suggest that the ARIMA + LSTM is more accurate in predicting the trend of CO2 emissions in China. Moreover, the ARIMA + LSTM is employed to forecast the future CO2 emission trends in China's east, central, and west regions, which can serve as a foundation for China's CO2 emission reduction initiatives.

Does climate policy uncertainty affect carbon emissions in China? A novel dynamic ARDL simulation perspective

This study provides new insights into the impact of climate policy uncertainty, energy consumption, and economic development on China’s carbon emissions. In doing so, we develop a novel index of China’s climate policy uncertainty (CCPU). We then use the newly constructed dynamic autoregressive distributed lag (ARDL) simulation model, the frequency-domain causality (FDC) test, and the fully modified OLS (FMOLS) estimation to investigate these potential relationships from 2005 to 2021. The empirical results suggest that increasing CCPU reduces carbon emissions in most parts of China, which improves environmental degradation. Furthermore, the effects of energy consumption and economic growth on carbon emissions are confirmed to be positive in each location. Finally, the results of the FDC and FMOLS confirm the robustness of the model. Our findings suggest that information from the CCPU can be used to forecast CO2 emissions in China. Furthermore, the government should strike a balance between economic growth and environmental regulation and promote the use of renewable energy to reduce carbon emissions. Proactively developing climate policy is important to achieve the goal of carbon neutrality.

Accurate forecasts and comparative analysis of Chinese CO2 emissions using a superior time-delay grey model

In China's new development stage, reaching carbon peaking and neutrality has emerged as a complicated and substantial task, highlighting the importance of forecasting CO2 emissions. Therefore, this research designs a novel convolution-based multivariate time-delay grey model that integrates time-delay coefficients and analytical solutions, thus accurately identifying and quantifying time-delay effects. Moreover, we present an algorithm determination framework to enhance the model's applicability and flexibility, and assess the robustness through the Monte Carlo Simulation and parameter sensitivity analysis. For demonstration purposes, the novel technique is utilized to forecast Chinese CO2 emissions, yielding the lowest MAPE values (1.80%for three-steps ahead, 1.51% for two-steps ahead, and 0.27% for one-step ahead) compared with traditional grey and non-grey competitor models. Additionally, the Monte Carlo Simulation results reveal that the particle swarm optimization algorithm outperforms other candidate algorithms due to the highest accuracy and optimal stability. Overall, the newly-proposed approach, possessing more powerful flexibility and applicability than the previous models, is an effective technique for forecasting CO2 emissions in China.

Forecasting CO2 emissions from road fuel combustion using grey prediction models: A novel approach

This paper proposes an optimized wavelet transform Hausdorff multivariate grey model (OWTHGM(1,N)) that addresses some of the weaknesses of the conventional GM(1,N) model such as inaccurate prediction and poor stability. Three improvements have been made: First, all inputs are filtered using a wavelet transform; second, a new time response function is established using the Hausdorff derivative; and finally, the use of Rao's algorithm to optimise the model's parameters as well as the ξ-order accumulated value of the observation data described by the Hausdorff derivative. In order to demonstrate the effectiveness of OWTHGM(1,N), it is applied to predict CO2 emissions from road fuel combustion. The new model scores 1.27% MAPE and 79.983 RMSE, and is therefore more accurate than competing models. OWTHGM(1,N) could therefore serve a reliable forecasting tool and used to monitor the evolution of CO2 emissions in Cameroon. The forecast results also serve as a sound foundation for the formulation of energy consumption strategies and environmental policies.• Modification, extension and optimization of grey multivariate model is done.• The model is very generic can be applied to a wide variety of energy sectors.• OWTHGM(1,N) is a valid forecasting tool that can be used to track CO2 emissions.

Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port

IntroductionIn recent years, the adverse effects of escalating maritime trade and international shipping– particularly in regard to increased greenhouse gas emissions and their impact on human health– have come to the fore. These issues have thus instigated a surge in pressure to enhance the regulation of shipborne carbon emissions.MethodsThe study utilized the automatic identification system (AIS) data, Lloyd’s register data, and pollutant emission parameters to calculate the carbon emissions from the main engine, auxiliary engine, and boiler of vessels under varying sailing conditions, utilizing the dynamic method of ships. In relation to geographic information and ship trajectory, a comprehensive inventory of ship carbon emissions was developed, revealing pronounced spatiotemporal characteristics. To assure the accuracy of the substantial AIS dataset, procedures including data cleaning, trajectory integration, data fusion, and completion were executed. Such processes are indispensable, given the potential for transmission and storage errors associated with AIS data. To forecast CO2 emissions over diverse time intervals, a temporal fusion transformer model equipped with attention mechanisms was employed.ResultThe paper furnishes a case study on Tianjin Port, wherein a high-resolution carbon emissions inventory was devised based on AIS data acquired from vessels. This inventory was subsequently employed to generate multi-feature predictions of future carbon emissions. Given the optimal parameter configuration, the proposed method attained P50 and P90 values of 0.244 and 0.118 respectively, thereby demonstrating its efficacy.DiscussionRecognizing the sources of ship carbon emissions in this region and forecasting such emissions in the future substantiates that this method accurately portrays the laws of ship carbon emissions. Our study provides a scientific basis for decision-making in port and pollution management, enabling the creation of targeted emission reduction policies for ships.

Analyzing global renewable energy generation trends amid volatile economic and social environments

As renewable energy (RE) rapidly integrates into society to meet the growing demand for affordable and clean energy (SDG 7, United Nations’ Sustainable Development Goal 7), it is crucial to analyze the merits and flaws of renewable energy generation by assessing its impact on the global economy and social wellbeing. This paper performs an investigative study on the correlation and causality between renewable energy, economy, and environmental indicators. The data was gathered from diverse sources, including The Center for Climate and Energy Solutions, National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA GISS), and Our World in Data, for the temperature, carbon dioxide (CO2) emissions, and gross domestic product (GDP) data. Significant Granger p-values were obtained for RE generation’s ability to forecast CO2 emissions and temperature, while discovering a strong positive correlation between CO2 and RE generation. The findings revealed that RE has limited effects on the global economy but has considerable implications on social and ecological well-being.

Use of random forest based on the effects of urban governance elements to forecast CO2 emissions in Chinese cities

Chinese cities contributes a large amount of CO2 emissions. Reducing CO2 emissions through urban governance is an important issue. Despite the increasing attention paid on the CO2 emission prediction, few studies consider the collective and complex influence of governance element system. To predict and regulate CO2 emissions through comprehensive urban governance elements, this paper use the random forest model through the data from 1903 Chinese county-level cities in 2010, 2012 and 2015, and establish a CO2 forecasting platform based on the effects of urban governance elements. The results are as follows: (1) The municipal utility facilities element, the economic development & industrial structure element, and the city size &structure and road traffic facilities elements are crucial for residential, industrial and transportation CO2 emissions, respectively; (2) Governance elements have nonlinear relationship with CO2 emissions and most of the relations present obvious threshold effects; (3) Random forest can be used to predict CO2 emissions more accurately than can other predictive models. These findings can be used to conducts the CO2 scenario simulation and help government formulate active governance measurements.

A time-varying approach to the nexus between environmental related technologies, renewable energy consumption and environmental sustainability in South Africa

Ecological degradation is a major challenge for all nations. The problem is particularly worrying for South Africa, which has recently suffered from various ecological catastrophes. Thus, the empirical study evaluates the nexus between CO2 emissions and financial development, renewable energy, economic growth and environmental-related technologies in South Africa utilizing data between 1980 and 2020. We employed autoregressive distributed lag (ARDL) and time-varying causality to evaluate these connections. The results from the ARDL show that financial development and environmental-related technologies lessen CO2 emissions while economic progress intensifies CO2 emissions. Surprisingly, renewable energy does not mitigate CO2 emissions. Furthermore, the time-varying causality shows that all the independent variables can forecast CO2 emissions at different sub-periods. Finally, our results are resilient to various policy ramifications useful in reducing CO2 emissions and associated adverse ecological consequences.