scholarly journals A Multivariate Grey Prediction Model Using Neural Networks with Application to Carbon Dioxide Emissions Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yu-Jing Chiu ◽  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Jingci Xie ◽  
Yen-Wei Ken
Keyword(s):  
Carbon Dioxide ◽  
Prediction Model ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Proposed Model ◽  
Predicted Values ◽  
Carbon Dioxide Co2 ◽  
Grey Relational

The forecast of carbon dioxide (CO2) emissions has played a significant role in drawing up energy development policies for individual countries. Since data about CO2 emissions are often limited and do not conform to the usual statistical assumptions, this study attempts to develop a novel multivariate grey prediction model (MGPM) for CO2 emissions. Compared with other MGPMs, the proposed model has several distinctive features. First, both feature selection and residual modification are considered to improve prediction accuracy. For the former, grey relational analysis is used to filter out the irrelevant features that have weaker relevance with CO2 emissions. For the latter, predicted values obtained from the proposed MGPM are further adjusted by establishing a neural-network-based residual model. Prediction accuracies of the proposed MGPM were verified using real CO2 emission cases. Experimental results demonstrated that the proposed MGPM performed well compared with other MGPMs considered.

scholarly journals An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting

2021 ◽  
Vol 18 (2) ◽  
pp. 587
Author(s):  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Jung-Fa Tsai ◽  
Ching-Ying Yu
Keyword(s):  
Carbon Dioxide ◽  
Prediction Model ◽  
Carbon Dioxide Emissions ◽  
Prediction Models ◽  
International Energy Agency ◽  
Carbon Dioxide Emission ◽  
Control Variable ◽  
Grey Prediction ◽  
Emission Data ◽  
Grey Prediction Model

Because grey prediction does not demand that the collected data have to be in line with any statistical distribution, it is pertinent to set up grey prediction models for real-world problems. GM(1,1) has been a widely used grey prediction model, but relevant parameters, including the control variable and developing coefficient, rely on background values that are not easily determined. Furthermore, one-order accumulation is usually incorporated into grey prediction models, which assigns equal weights to each sample, to recognize regularities embedded in data sequences. Therefore, to optimize grey prediction models, this study employed a genetic algorithm to determine the relevant parameters and assigned appropriate weights to the sample data using fractional-order accumulation. Experimental results on the carbon dioxide emission data reported by the International Energy Agency demonstrated that the proposed grey prediction model was significantly superior to the other considered prediction models.

scholarly journals Estimation of Carbon Dioxide Emissions from a Traditional Nutrient-Rich Cambodian Diet Food Production System Using Life Cycle Assessment

2021 ◽  
Vol 13 (7) ◽  
pp. 3660
Author(s):  
Rathna Hor ◽  
Phanna Ly ◽  
Agusta Samodra Putra ◽  
Riaru Ishizaki ◽  
Tofael Ahamed ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Co2 Emissions ◽  
Production System ◽  
Agricultural Production ◽  
Carbon Dioxide Emissions ◽  
Food Production ◽  
Carbon Dioxide Co2 ◽  
Food Production System

Traditional Cambodian food has higher nutrient balances and is environmentally sustainable compared to conventional diets. However, there is a lack of knowledge and evidence on nutrient intake and the environmental greenness of traditional food at different age distributions. The relationship between nutritional intake and environmental impact can be evaluated using carbon dioxide (CO2) emissions from agricultural production based on life cycle assessment (LCA). The objective of this study was to estimate the CO2 equivalent (eq) emissions from the traditional Cambodian diet using LCA, starting at each agricultural production phase. A one-year food consumption scenario with the traditional diet was established. Five breakfast (BF1–5) and seven lunch and dinner (LD1–7) food sets were consumed at the same rate and compared using LCA. The results showed that BF1 and LD2 had the lowest and highest emissions (0.3 Mt CO2 eq/yr and 1.2 Mt CO2 eq/yr, respectively). The food calories, minerals, and vitamins met the recommended dietary allowance. The country’s existing food production system generates CO2 emissions of 9.7 Mt CO2 eq/yr, with the proposed system reducing these by 28.9% to 6.9 Mt CO2 eq/yr. The change in each food item could decrease emissions depending on the type and quantity of the food set, especially meat and milk consumption.

scholarly journals The travel-related carbon dioxide emissions of atmospheric researchers

2008 ◽  
Vol 8 (2) ◽  
pp. 7373-7389 ◽  
Author(s):  
A. Stohl
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Information Exchange ◽  
Carbon Dioxide Emissions ◽  
Radiative Forcing ◽  
Global Climate ◽  
Air Travel ◽  
Ozone Production ◽  
Per Capita ◽  
Carbon Dioxide Co2

Abstract. Most atmospheric scientists agree that greenhouse gas emissions have already caused significant changes to the global climate system and that these changes will accelerate in the near future. At the same time, atmospheric scientists who – like other scientists – rely on international collaboration and information exchange travel a lot and, thereby, cause substantial emissions of carbon dioxide (CO2). In this paper, the CO2 emissions of the employees working at an atmospheric research institute (the Norwegian Institute for Air Research, NILU) caused by all types of business travel (conference visits, workshops, field campaigns, instrument maintainance, etc.) were calculated for the years 2005–2007. It is estimated that more than 90% of the emissions were caused by air travel, 3% by ground travel and 5% by hotel usage. The travel-related annual emissions were between 1.9 and 2.4 t CO2 per employee or between 3.9 and 5.5 t CO2 per scientist. For comparison, the total annual per capita CO2 emissions are 4.5 t worldwide, 1.2 t for India, 3.8 t for China, 5.9 t for Sweden and 19.1 t for Norway. The travel-related CO2 emissions of a NILU scientist, occurring in 24 days of a year on average, exceed the global average annual per capita emission. Norway's per-capita CO2 emissions are among the highest in the world, mostly because of the emissions from the oil industry. If the emissions per NILU scientist derived in this paper are taken as representative for the average Norwegian researcher, travel by Norwegian scientists would nevertheless account for a substantial 0.2% of Norway's total CO2 emissions. Since most of the travel-related emissions are due to air travel, water vapor emissions, ozone production and contrail formation further increase the relative importance of NILU's travel in terms of radiative forcing.

scholarly journals A Hybrid Grey Prediction Model for Small Oscillation Sequence Based on Information Decomposition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Meng Zhou ◽  
Bo Zeng ◽  
Wenhao Zhou
Keyword(s):  
Prediction Model ◽  
Small Oscillation ◽  
New Method ◽  
Superior Performance ◽  
Small Data ◽  
Grey Prediction ◽  
New Model ◽  
Grey Prediction Model ◽  
Proposed Model ◽  
Oscillation Characteristic

Grey prediction model has good performance in solving small data problem, and has been widely used in various research fields. However, when the data show oscillation characteristic, the effect of grey prediction model performs poor. To this end, a new method was proposed to solve the problem of modelling small data oscillation sequence with grey prediction model. Based on the idea of information decomposition, the new method employed grey prediction model to capture the trend characteristic of complex system, and ARMA model was applied to describe the random oscillation characteristic of the system. Crops disaster area in China was selected as a case study and the relevant historical eight-year data published by government department were substituted to the proposed model. The modelling results of the new model were compared with those of other traditional mainstream prediction models. The results showed that the new model had evidently superior performance. It indicated that the proposed model will contribute to solve small oscillation problems and have positive significance for improving the applicability of grey prediction model.

Estimating Chinese energy-related CO2 emissions by employing a novel discrete grey prediction model

2020 ◽  
Vol 259 ◽  
pp. 120793 ◽  
Author(s):  
Song Ding ◽  
Ning Xu ◽  
Jing Ye ◽  
Weijie Zhou ◽  
Xiaoxiong Zhang
Keyword(s):  
Prediction Model ◽  
Co2 Emissions ◽  
Grey Prediction ◽  
Grey Prediction Model

scholarly journals Analysis of Carbon Dioxide Concentration Prediction Model and Diffusion Tendency of Expiratory by Simultaneous Multipoint Sensing

2020 ◽  
Vol 10 (13) ◽  
pp. 4631
Author(s):  
Motokazu Moritani ◽  
Norifumi Watanabe ◽  
Kensuke Miyamoto ◽  
Kota Itoda ◽  
Junya Imani ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Air Quality ◽  
Prediction Model ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Carbon Dioxide Concentration ◽  
Predicted Values ◽  
Carbon Dioxide Co2 ◽  
Intellectual Activities ◽  
And Diffusion

Recent indoor air quality studies show that even 1000 parts per million (ppm) concentration of Carbon Dioxide (CO2) has an adverse effect on human intellectual activities. Therefore, it is required to keep the CO 2 concentration below a certain value in a room. In this study, in order to analyze the diffusion tendency of carbon dioxide by breathing, we constructed a simultaneous multi-point sensing system equipped with a carbon dioxide concentration sensor to measure indoor environment. Furthermore, it was evaluated whether the prediction model can be effectively used by comparing the prediction value by the model and the actually measured value from the sensor. The experimental results showed that CO 2 by exhaled breathing diffuses evenly throughout the room regardless of the sensor’s relative positions to the human test subjects. The existing model is sufficiently accurate in a room which has above at least a 0.67 cycle/h ventilation cycle. However, there is a large gap between the measured and the model’s predicted values in a room with a low ventilation cycle, and that suggests a measurement with a sensor still is necessary to precisely monitor the indoor air quality.

scholarly journals Designing a Statistical Procedure for Monitoring Global Carbon Dioxide Emissions

2021 ◽  
Author(s):  
Mikkel Bennedsen
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Testing Procedure ◽  
Paris Agreement ◽  
Sequential Testing ◽  
Monitoring Procedure ◽  
Nationally Determined Contributions ◽  
Carbon Dioxide Co2 ◽  
Global Carbon

Abstract Following the Paris Agreement of 2015, most countries have agreed to reduce their carbon dioxide (CO2) emissions according to individually set Nationally Determined Contributions. However, national CO2 emissions are reported by individual countries and cannot be directly measured or verified by third parties. Inherent weaknesses in the reporting methodology may misrepresent, typically an under-reporting of, the total national emissions. This paper applies the theory of sequential testing to design a statistical monitoring procedure that can be used to detect systematic under-reportings of CO2 emissions. Using simulations, we investigate how the proposed sequential testing procedure can be expected to work in practice. We find that, if emissions are reported faithfully, the test is correctly sized, while, if emissions are under-reported, detection time can be sufficiently fast to help inform the 5 yearly global "stocktake" of the Paris Agreement. We recommend the monitoring procedure be applied going forward as part of a larger portfolio of methods designed to verify future global CO2 emissions.

scholarly journals Observing carbon dioxide emissions over China's cities and industrial areas with the Orbiting Carbon Observatory-2

2020 ◽  
Vol 20 (14) ◽  
pp. 8501-8510 ◽  
Author(s):  
Bo Zheng ◽  
Frédéric Chevallier ◽  
Philippe Ciais ◽  
Grégoire Broquet ◽  
Yilong Wang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Power Plants ◽  
Global Climate ◽  
Large Cities ◽  
Independent Evaluation ◽  
Paris Climate Agreement ◽  
Carbon Dioxide Co2 ◽  
The Individual

Abstract. In order to track progress towards the global climate targets, the parties that signed the Paris Climate Agreement will regularly report their anthropogenic carbon dioxide (CO2) emissions based on energy statistics and CO2 emission factors. Independent evaluation of this self-reporting system is a fast-growing research topic. Here, we study the value of satellite observations of the column CO2 concentrations to estimate CO2 anthropogenic emissions with 5 years of the Orbiting Carbon Observatory-2 (OCO-2) retrievals over and around China. With the detailed information of emission source locations and the local wind, we successfully observe CO2 plumes from 46 cities and industrial regions over China and quantify their CO2 emissions from the OCO-2 observations, which add up to a total of 1.3 Gt CO2 yr−1 that accounts for approximately 13 % of mainland China's annual emissions. The number of cities whose emissions are constrained by OCO-2 here is 3 to 10 times larger than in previous studies that only focused on large cities and power plants in different locations around the world. Our satellite-based emission estimates are broadly consistent with the independent values from China's detailed emission inventory MEIC but are more different from those of two widely used global gridded emission datasets (i.e., EDGAR and ODIAC), especially for the emission estimates for the individual cities. These results demonstrate some skill in the satellite-based emission quantification for isolated source clusters with the OCO-2, despite the sparse sampling of this instrument not designed for this purpose. This skill can be improved by future satellite missions that will have a denser spatial sampling of surface emitting areas, which will come soon in the early 2020s.

A novel adaptive discrete grey prediction model for forecasting development in energy consumption structure—from the perspective of compositional data

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wuyong Qian ◽  
Hao Zhang ◽  
Aodi Sui ◽  
Yuhong Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Compositional Data ◽  
Development Trend ◽  
Grey Prediction ◽  
Content Type ◽  
Consumption Structure ◽  
Grey Prediction Model ◽  
Proposed Model

PurposeThe purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.Design/methodology/approachDue to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.FindingsChina's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.Originality/valueThe paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

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