scholarly journals Forecasting short-term carbon emission futures price volatility: information for hedging carbon emission futures risk

2017 ◽  
Vol 8 (4) ◽  
pp. 6-13 ◽  
Author(s):  
Collins C. Ngwakwe
Keyword(s):  
Environmental Economics ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Time Series Data ◽  
Price Volatility ◽  
Series Data ◽  
Carbon Markets ◽  
Short Term ◽  
Futures Price ◽  
Short Term Forecasting

This paper aimed to illustrate how short-term carbon futures speculators might use short-term carbon emission futures data to predict and forecast carbon prices. The paper became apposite given ubiquitous research focussing on long-term carbon futures data, which has left out short-term carbon emission futures speculators with information. Therefore, this paper demonstrated that short-term speculators in carbon futures could indeed use short-term time series data on carbon futures to make a reliable prediction and forecasting of carbon emissions futures price volatility within a short term and thus decide on investment opportunity. The sample data results showed that short-term data could produce a dependable in-sample futures prediction since the in-sample prediction fell within the 95% confidence interval. The demonstration also showed that short-term carbon futures data could assist speculators to conduct a reliable short-term out of sample forecast of carbon futures prices within the closer period. The paper offers practical assistance to carbon futures speculators and is equally important for academic studies for business and economic students on discussions and research bordering on carbon emissions, carbon trading, environmental economics and sustainable development. More carbon short-term forecasting is encouraged – such research should compare short-term forecasting of carbon futures amongst different carbon markets.

scholarly journals Does urbanization intensify carbon emissions in Nigeria?

2020 ◽  
Vol 17 (2) ◽  
pp. 161-177
Author(s):  
Muhammad Shehu
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Use ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Short Term ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag

This study examines the urbanization and CO2 emissions nexus in Nigeria using the Autoregressive Distributed Lag (ARDL) method to analyze the annual time series data spanning from 1974 to 2015. Findings suggest that urbanization, GDP, energy use, and carbon emissions are strongly and positively correlated, while trade and carbon emissions exhibit a weak and negative correlation. The ARDL result shows a negatively significant short-term and long-term connection between urbanization and carbon emission in the Nigerian economy. In the short-term, GDP, trade and energy use positively affect carbon emission while in the long-term, trade and GDP negatively affect carbon emissions with energy use having a positive impact on carbon emissions. The study, therefore, concludes that urbanization does not cause carbon emission to rise in Nigeria, but energy use does. From the findings, it was recommended that there is a need for the use of energy-saving and environmentally friendly technology to reduce the amount of carbon emission in the economy.

The Decomposition Analysis of Carbon Emissions: Theoretical Basis, Methods and Their Evaluations

2020 ◽  
Vol 08 (04) ◽  
pp. 2050020
Author(s):  
Shenning QU
Keyword(s):  
Environmental Economics ◽  
Carbon Emissions ◽  
Time Series Data ◽  
Decomposition Analysis ◽  
Series Data ◽  
Process Time ◽  
Structural Decomposition Analysis ◽  
Systematic Analysis ◽  
Index Decomposition ◽  
Laspeyres Index

As an analytical framework for studying the characteristics of changes in things and their action mechanisms, the decomposition analysis of greenhouse gas emissions has been increasingly used in environmental economics research. The author introduces several decomposition methods commonly used at present and compares them. The index decomposition analysis (IDA) of carbon emissions usually uses energy identities to express carbon emissions as the product of several factor indexes, and decomposes them according to different weight-determining methods to clarify the incremental share of each index, in which way it is possible to decompose the models that contain less factors, process time series data, and conduct cross-country comparisons. It mainly includes the Laspeyres index decomposition and the Divisia index decomposition. Among them, the LMDI I method has been widely used for its advantages such as generating no residuals and easy to use. The structural decomposition analysis (SDA) can be used to conduct a more systematic analysis, decompose models with more influencing factors, and analyze the impacts of various factors on emissions, but this method has higher requirements for data collection. The biggest difference between the SDA method and the IDA methods of carbon emissions is that the former is based on an input–output system, while the latter only needs to use sectors’ aggregate data.

Research on the Relationship between Economic Growth, Environmental Governance Investment and Carbon Emission--Based on the Time Series Data from 2000 to 2019

2020 ◽  
pp. 713-727
Author(s):  
Xiaohui Wang, Xin Zhang
Keyword(s):  
Economic Growth ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Carbon Emission ◽  
Environmental Governance ◽  
Time Series Data ◽  
Series Data ◽  
Per Capita ◽  
The Relationship

The study on the relationship between investment in environmental governance, carbon emission and economic growth is helpful for the relevant government departments to coordinate the influence among them when formulating the policies of reducing emission and conserving energy, so as to take the comparative advantages of various factors and promote the benign interaction between economic development and environmental governance. In this paper, the data of Per capita GDP, per capita investment in environmental governance and per capita CARBON dioxide emissions in China from 2000 to 2019 are selected as the research basis, and variables are studied by means of Granger causality and impulse response function. As shown in the results, there is a single Granger relationship between investment in environmental governance and carbon emissions, that is, the increase of investment in environmental governance leads to the reduction of carbon emissions. The influence of economic growth on environmental governance investment is small, but in the long term, it can restrain the growth of carbon emissions. Investment in environmental governance can promote economic growth and stimulate a reduction in the emissions in the short term; Economic growth was hindered by the emissions in the long term and fail to stimulate increased investment in environmental governance. Based on these findings, this paper proposes policy Suggestions for optimizing the structure of environmental governance investment, improving the carbon emission monitoring and response mechanism, and strengthening the technological level of energy conservation and emission reduction.

scholarly journals STUDI PERAMALAN BEBAN RATA – RATA JANGKA PENDEK MENGGUNAKAN METODA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA

Sutet ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 93-101
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Power Plants ◽  
Time Series Data ◽  
Moving Average ◽  
Current Model ◽  
Series Data ◽  
Electrical Load ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Independent Variable ◽  
Short Term Forecasting

Forecasting. Plans, power plants ,. Electricity needs are increasingly changing daily, so the State Electricity Company (PLN) as a provider of energy must be able to predict daily electricity needs. Short-term forecasting is the prediction of electricity demand for a certain period of time ranging from a few minutes to a week ahead. in shortterm electrical forecasting much of the literature describes the techniques and methods applied in forecasting, Autoregresive Integrated Moving Average (ARIMA), linear regression, and artificial intelligence such as Artificial Neural Networks and fuzzy logic. Short-term forecasting will be done by the authors using time series data that is the data of the use of electric power daily (electrical load) and ARIMA as a method of forecasting. ARIMA method or often called Box-Jenkins technique to find this method is suitable to predict variable costs quickly, simply, and cheaply because it only requires data variables to be predicted. ARIMA can only be used for short-term forecasting. ARIMA is a special linear test, in the form of forecasting this model is completely independent variable variables because this model uses the current model and past values of the dependent variable to produce an accurate short-term forecast.

scholarly journals Application of GARCH Forecasting Method in Predicting The Number of Rail Passengers (Thousands of People) in Jabodetabek Region

2022 ◽  
Vol 18 (2) ◽  
pp. 198-223
Author(s):  
Farin Cyntiya Garini ◽  
Warosatul Anbiya
Keyword(s):  
Time Series ◽  
Public Transportation ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Method ◽  
Short Term ◽  
Seasonal Factors ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Over Time

PT. Kereta Api Indonesia and PT. KAI Commuter Jabodetabek records time series data in the form of the number of train passengers (thousand people) in Jabodetabek Region in 2011-2020. One of the time series methods that can be used to predict the number of train passengers (thousand people) in Jabodetabek area is ARIMA method. ARIMA or also known as Box-Jenkins time series analysis method is used for short-term forecasting and does not accommodate seasonal factors. If the assumption of residual homoscedasticity is violated, the ARCH / GARCH method can be used, which explicitly models changes in residual variety over time. This study aims to model and forecast the number of train passengers (thousand people) in Jabodetabek area in 2021. Based on data analysis and processing using ARIMA method, the best model is ARIMA (1,1,1) with an AIC value of 2,159.87 and with ARCH / GARCH method, the best model is GARCH (1,1) with an AIC value of 18.314. Forecasting results obtained based on the best model can be used as a reference for related parties in managing and providing public transportation facilities, especially trains.

scholarly journals Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 11 ◽  
Author(s):  
María Carmen Ruiz-Abellón ◽  
Luis Alfredo Fernández-Jiménez ◽  
Antonio Guillamón ◽  
Alberto Falces ◽  
Ana García-Garre ◽  
...  
Keyword(s):  
Power Systems ◽  
Demand Response ◽  
Time Series Data ◽  
Load Forecasting ◽  
Real Data ◽  
Series Data ◽  
Renewable Generation ◽  
Short Term ◽  
Forecasting Models ◽  
Short Term Forecasting

The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure.

scholarly journals Short-Term Forecasting Model of Animal Food Commodities in Central Sulawesi

2020 ◽  
Vol 8 (1) ◽  
pp. 17
Author(s):  
Rustam Abdul Rauf ◽  
Dian Safitri ◽  
Christoporus Christoporus ◽  
Effendy Effendy ◽  
Muhardi Muhardi
Keyword(s):  
Time Series Data ◽  
Supply And Demand ◽  
Series Data ◽  
Forecasting Model ◽  
Short Term ◽  
Broiler Meat ◽  
Animal Food ◽  
Short Term Forecasting ◽  
Food Commodities ◽  
Central Sulawesi

Shifting patterns of community consumption from vegetable protein to animal protein encouraged high demand for animal food, so it was needed an estimate of the supply and demand for its products. Therefore, this research aimed to analyze the short-term forecasting model of the production and price of beef and broiler meat in Central Sulawesi. The research used time series data. Production data and price of beef and broiler meat were taken from 2015 - 2019. The analytical tool used was the ARIMA Box-Janskin forecasting method. The results showed a short-term forecasting model for beef production (1,0,0) and broiler meat (3,2,1). Short-term forecasting model for beef price (1,0,1) and broiler meat (1,1,1).  This finding could be used as a reference in making policies related to the production and price of beef and broilers meat in order to meet the needs of the community, especially in Central Sulawesi.

scholarly journals Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1339
Author(s):  
Ziyuan Chai ◽  
Zibibula Simayi ◽  
Zhihan Yang ◽  
Shengtian Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Driving Factors ◽  
Series Data ◽  
Lake Basin ◽  
Stirpat Model ◽  
Consumption Structure ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

Sign in / Sign up

Export Citation Format

Share Document