Forecasting CO2 Emissions Research Articles

Trends in CO2 Emissions from China-Oriented International Marine Transportation Activities and Policy Implications

The demand for marine transportation and its associated CO2 emissions are growing rapidly as a result of increasing international trade and economic growth. An activity-based approach is developed for forecasting CO2 emissions from the China-oriented international seaborne trade sector. To accurately estimate the aggregated emissions, CO2 emissions are calculated individually for five categories of vessels: crude oil tanker, product tanker, chemical tanker, bulk carrier, and container. A business-as-usual (BAU) scenario was developed to describe the current situation without additional mitigation policies, whilst three alternative scenarios were developed to describe scenarios with various accelerated improvements of the key factors. The aggregated CO2 emissions are predicted to reach 419.97 Mt under the BAU scenario, and 258.47 Mt under the optimal case, AD3. These predictions are 4.5 times and 2.8 times that of the aggregated emissions in 2007. Our analysis suggests that regulations for monitoring, reporting, and verifying the activities of vessels should be proposed, in order to quantify the CO2 emissions of marine transportation activities in Chinese territorial waters. In the long-term future, mitigation policies should be employed to reduce CO2 emissions from the marine trade sector and to address the climatic impact of shipping.

Forecasting Chinese CO2 emissions from fuel combustion using a novel grey multivariable model

Forecasting CO2 emissions in China always has been of great significance as it could help the government to improve energy policies and plans. To this end, a novel grey multivariable model is designed in this paper. Compared with the conventional grey multivariable model, which has certain drawbacks of inaccurate prediction and poor adaptability that restrict their applications in practical cases, the proposed model can make three improvements: first, an optimized grey model having a modified background value is proposed to predict the trends of the driving variables. Second, the novel grey multivariable model is established, combined with the changing trends of driving variables. Third, the adjustment coefficient in the new model is optimized to obtain optimal values for the time response function. To demonstrate its efficacy, the proposed model is employed to reproduce and predict the CO2 emissions from fuel combustion compared with four benchmark models—the results show that the new model yields more accurate forecasting results than the competing models. Eventually, the new model will be used to quantify future Chinese CO2 emissions from fuel combustion from 2014 to 2020, and the forecasted results can provide a solid basis for formulating environmental policies and energy consumption plans.

Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization

In the prevailing low-carbon economy, China is under enormous pressure to control CO2 emissions, therefore, of great significance is the study to analyze what influential factors mainly contribute to emissions, so as to forecast emissions accurately and harness the growth from the source. In this paper, basing on 22 influencing factors identified by bivariate correlation analysis, factor analysis is then adopted to extract the latent factors which essentially affect emissions and 8 special factors transformed by scoring coefficients are acquired. Extreme learning machine (ELM) whose input weights and bias threshold were optimized by particle swarm optimization (PSO), hereafter referred as PSO-ELM, is established to predict CO2 emissions and testify the availability of the factor analysis. Case studies reveal that the factor analysis which generates 8 factors as input can highly improve prediction accuracy. And the simulation results demonstrate that the built model PSO-ELM outperforms the compared ELM and back propagation neural network in forecasting CO2 emissions. Eventually, the analysis made in this study can provide valuable policy implications for Hebei’s CO2 emissions reduction and strategic low-carbon development.

Forecasting CO2 emissions in the Persian Gulf States.

The Persian Gulf States (Bahrain. Iran, Iraq, Kuwait, Qatar, Saudi Arabia and United Arab Emirate) have dominated the oil and gas sector since the discovery of oil in the region. They are the world largest producers of crude oil, producing about 35 and 25 percent of the world natural gas and crude oil respectively. The use of fossil fuels is directly linked to the release of CO2 into the environment. CO2 accounts for 58.8 percent of all greenhouse gases released via human activities, consequently, presenting a malign impact on the environment through climate change, global warming, biodiversity, acid rain and desertification among others. Due to its importance, the data on CO2 emission obtained from US EIA from 1980 – 2010 was regressed using least square techniques and projections were made to the year 2050. Results indicated that each country’s p-value was less than 0.05 which implies that the models can be used for predicting CO2 emissions into the future. The data shows the emission of CO2 by countries from the highest to the lowest in 2016 as: Iran (590.72 Mtonnes; 7.58 tonnes of CO2/person) > Saudi Arabia (471.82 Mtonnes; 18 tonnes of CO2/person) > UAE (218.58 Mtonnes; 41.31 tonnes of CO2/person) > Iraq (114.01 Mtonees; 3.71 tonnes of CO2/person) > Kuwait (92.58 Mtonnes; 36.31 tonnes of CO2/person) > Qatar (68.26 Mtonnes; 37 tonnes of CO2/person) > Bahrain (33.16 Mtonnes; 27.5 tonnes of CO2/person). The sequence from the country with highest emission (Iran) to the country with lowest emission (Bahrain) will remain the same until 2050. A projection depicting a 7.7 percent yearly increase in CO2 emission in the Persian Gulf States.

Forecasting CO<SUB align="right">2 emission of Turkey: swarm intelligence approaches

It is known that among the various greenhouse gases, CO2 is the most frequently implicated in global warming and climate change. Therefore, there is a need for developing efficient quantitative tools that allow forecasting CO2 emissions. This paper presents application of particle swarm optimisation (PSO) and artificial bee colony (ABC) techniques to estimate CO2 emission in Turkey, based on socio-economic indicators. The models are developed in three forms which are linear, exponential and quadratic. PSOCO2 and ABCCO2 (PSO and ABC CO2 estimation models) are developed to estimate the future CO2 emission values based on energy consumption, population, gross domestic product (GDP), and number of motor vehicles data. Emitted CO2 emission in Turkey from 1980 to 2008 is considered as the case of this study. While the first 25 years data of 29 years data is used for validation of four models, full data is used for future projections. Sum square error (SSE) is used as a fitness function in proposed models. Finally, CO2 emission in Turkey is forecasted up to year 2030 under different scenarios.

Predicting CO2 Emissions from Farm Inputs in Wheat Production using Artificial Neural Networks and Linear Regression Models

Two models have been developed for simulating CO2 emissions from wheat farms: (1) an artificial neural network (ANN) model; and (2) a multiple linear regression model (MLR). Data were collected from 40 wheat farms in the Canterbury region of New Zealand. Investigation of more than 140 various factors enabled the selection of eight factors to be employed as the independent variables for final the ANN model. The results showed the final ANN developed can forecast CO2 emissions from wheat production areas under different conditions (proportion of wheat cultivated land on the farm, numbers of irrigation applications and numbers of cows), the condition of machinery (tractor power index (hp/ha) and age of fertilizer spreader) and N, P and insecticide inputs on the farms with an accuracy of ±11% (± 113 kg CO2/ha). The total CO2 emissions from farm inputs were estimated as 1032 kg CO2/ha for wheat production. On average, fertilizer use of 52% and fuel use of around 20% have the highest CO2 emissions for wheat cultivation. The results confirmed the ANN model forecast CO2 emissions much better than MLR model.

Growing green? Forecasting CO2 emissions with Environmental Kuznets Curves and Logistic Growth Models

Abstract Environmental sustainability was established by the United Nations as a Millennium Development Goal (MDG7), including a wide variety of targets referred to the access to safe drinking water, the reduction of biodiversity loss, the improvement of lives of slum dwellers and the integration of principles of sustainable development into the country policies. Despite some progress towards meeting this goal, new challenges have appeared, endangering the development and environmental achievements. Therefore, United Nations and the international community are working on the Sustainable Development Goals (SDG) as a new framework placing environmental sustainability at its core. In this context, the analysis of environmental forecasts plays a main role. More specifically, taking into account that the carbon dioxide emissions have increased by almost 50 per cent since year 1990, special attention must be paid to the evolution and projections of emissions for different countries. In this paper we focus on environmental forecasting, based on the extended Environmental Kuznets Curve (EKC) and the Environmental Logistic Curve (ELC). Considering a sample of 175 countries we perform a competition between both methods, analysing their goodness of fit and their forecasting accuracy. The empirical results provide significant evidence about the adequacy of EKC and ELC for explaining CO2 emissions in different countries, also allowing us to obtain some ex-ante projections.

METHODS IN FORECASTING CARBON DIOXIDE EMISSIONS: A DECADE REVIEW

Analyses and forecasts of carbon dioxide (CO2) emissions is one of the key requirements to educate people about the issues of clean and healthy environment. Various methods have been proposed to forecast CO2 emissions. This paper reviews the literature of the methods for the forecasting as well as estimatingCO2 emissions. Related articles appearing in the international journals from 2003 to 2013 were gathered and analysed to find the answers for these two questions: (i) Which methods were prevalently applied? (ii) Which factors were regularly been investigated? Based on the overall observations on the journal articles some improvements and possible future works are recommended. This research not only provides evidence that the artificial intelligence methods are the most favour methods, but also aids the researchers and policy makers in applying the methods effectively.

Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model

This study reexamines the relationship between energy consumption, urban population, economic growth and CO2 emissions in the BRICS countries (i.e., Brazil, Russia, India, China, and South Africa) during the period 2004–2010, by using a novel multi-variable grey model. The results indicate that the economic growth has a decreasing effect on the CO2 emissions in Brazil and Russia and has an increasing effect in India, China and South Africa, and the CO2 emissions will increase with the increasing of urban population and energy consumption in all BRICS countries. The CO2 emissions in the BRICS countries are further predicted by rolling multi-variable grey forecasting model considering energy consumption and economic growth. China has the greatest growth rate in CO2 emissions, Brazil and India have the greater ones, while Russian Federation and South Africa have the least ones. The results not only contribute to advancing the existing literature, but also merit particular attention from policy makers in the BRICS countries.

Forecast of CO2 Emissions From the U.S. Transportation Sector: Estimation From a Double Exponential Smoothing Model

This study examines whether the decreasing trend in U.S. CO2 emissions from the transportation sector since the end of the 2000s will be shown across all states in the nation for 2012‒2021. A double exponential smoothing model is used to forecast CO2 emissions for the transportation sector in the 50 states and the U.S., and its findings are supported by the validity test of pseudo out-of-sample forecasts. We conclude that the decreasing trend in transportation CO2 emissions in the U.S. will continue in most states in the future.

Integrated CO2, cost, and schedule management system for building construction projects using the earned value management theory

Although the construction phase in a building's life cycle is relatively very short, the density of the CO2 emissions in the construction phase is very higher than that in the other phases (i.e., operations and maintenance phase). However, there was little research on scheduling, monitoring, evaluating and forecasting CO2 emissions based on the construction schedule. To address this challenge, this research aimed to develop an integrated CO2, cost and schedule management (ICCSM) system for building construction projects using the earned value management theory. The proposed ICCSM system consisted of five steps: (i) definition of the project scope and organization; (ii) project planning and scheduling; (iii) estimation and budgeting of the CO2 emissions and construction costs; (iv) establishment and weighting of the project performance measurement baseline; and (v) monitoring and forecasting of the project performance. The case study showed that the ICCSM system can evaluate and forecast the project performance earlier and more accurately, based on the construction schedule, (i) when integrated weighting was implemented, the project performance can be estimated faster (i.e., one day earlier); and (ii) when 0.8 × (cost performance index) + 0.2 × (schedule performance index) was implemented as the index for calculating an estimate at completion, the project performance can be more accurately predicted (i.e., error rate = −0.24%). The ICCSM system could allow a project manager to monitor and forecast CO2 emissions and costs based on the construction schedule, simultaneously.

A COMBINATION FORECAST FOR ENERGY-RELATEDCO2EMISSIONS IN THE UNITED STATES

This paper seeks to provide an alternative forecast to that provided by the Energy Information Administration (EIA) on energy-related monthly CO2 emissions in the United States. The data on CO2 emissions from petroleum, natural gas, coal and total fossil fuels obtained via the EIA covering the period January 2005 to November 2013 is analysed and then forecasted using ARIMA, Holt-Winters, and Exponential Smoothing prior to introducing the Singular Spectrum Analysis (SSA) technique for CO2 emissions forecasting. A new combination forecast (EIA-SSA) is also introduced by merging the SSA and EIA forecasts, and is seen outperforming all models including the EIA forecast. Finally, the EIA-SSA model is used to provide an alternative 12 month ahead outlook for US energy-related CO2 emissions from December 2013 to November 2014. This research is expected to influence the methodology adopted by the EIA for forecasting CO2 emissions in the future by improving the accuracy of the forecasts, and the impact of this study will be clearer upon comparing the actual CO2 emissions in US with the EIA, and EIA-SSA forecasts over the 12 month period which follows.

Forecast Global Carbon Dioxide Emission using Swarm Intelligence

The tremendous effects of air quality in large cities have been considered a severe environmental problem all over the world. Therefore, the international community agreed to develop air quality standards to monitor and control pollution rates around industrial communities. Harmful emission into the air is a sign that could extremely affect man’s health, natural life and agriculture. Forecasting models is essential for predicting air quality. CO2 emissions have been an international concern because of fossil fuels. In this study, Particle Swarm Optimization (PSO) is used for analyzing world CO2 emission based on the global energy consumption. A parametric PSO model is developed to forecast CO2 emission based set of attributes. They include: global oil, natural gas, coal, and primary energy consumption. A data set collected during the years 1980 and 2010 were used in this study. Experimental results show that PSO can provide good modeling results using a limited number of measurements compared to other linear models.

Application of Computer for Analyzing World Co2 Emission

Global climate change due to CO2 emissions is an issue of international concern that primarily attributed to fossil fuels. In this study, Genetic Algorithm (GA) is used for analyzing world CO2 emission based on the global energy consumption. Linear and non-linear forms of equations were developed to forecast CO2 emission using Genetic Algorithm (GA) based on the global oil, natural gas, coal, and primary energy consumption figures. The related data between 1980 and 2010 were used, partly for installing the models (finding candidates of best weighting factors for each model (1980-2003)) and partly for testing the models (2004–2010). Global CO2 emission is forecasted up to year 2030.

CO2 Reduction Policy Scenario in Transport Sector in Consideration of Consistency of Technological Innovations and Local Characteristics

The study has been undertaken with a view to formulate appropriate policies for targeted CO2 reduction from transport sector. For this a two step approach is adopted. As a first step, a model for forecasting CO2 emissions from passenger cars by municipalities is developed. This model takes into account the car ownership and trip length model. The application of the proposed model to the municipalities of Japan show that CO2 emissions will continue to increase at the national level and the increase of these emissions is remarkable in local municipalities. As a second step the targeted total CO2 emissions of Japan are allocated to different municipalities under different sets of policy scenarios. The examination of the five type of ESTI policy scenarios show that the target CO2 emissions cannot be achieved in most municipalities except metropolis. Eight types of EST2 policy scenarios, considering the local characteristics are therefore examined. The examination of the proposed EST2 policy scenarios and their mixed scenarios shows that more extensive policies are required since the targeted CO2 emissions cannot be achieved only by EST2 policies.

World Population Projections for Greenhouse Gas Emissions Scenarios

A survey is made of the latest world population projections issued by the United Nations, World Bank, U.S. Census Bureau, and International Institute for Applied Systems Analysis. Medium variants from all the organizations show excellent agreement with respect to many features of future world population growth. It appears that little would be gained by obtaining additional regional projections made by governments or organizations other than those listed above. In general, the new range of population projections that are candidates for forthcoming IPCC emissions scenarios are narrower and lower than the previous IPCC IS92 population range: a reflection of updated information on the decline of fertility rates in developing countries and the incorporation of a plausible correlation between mortality rates and fertility rates within the IIASA ‘rapid’ and ‘slow’ demographic transition variants. Comments are made on the schematic approach of forecasting CO2 emissions using multiplicative identities such as ‘IPAT’ (impact/emissions = population × affluence × technology). Although the unqualified IPAT model suggests that emissions should scale linearly with population, a number of caveats to this exist, the most important of which may be factor interactions. A brief review is made of conventional thinking about interactions between population growth and economic development. Correlation studies and theory suggest that population growth has a neutral or, at most, weak negative effect on economic growth. Conversely, it is well established that higher per capita incomes are well correlated with lower fertility and mortality rates in developing countries. Therefore, a plausible first-order relationship worth exploring in the next generation of IPCC scenarios is that scenarios with higher average economic growth rates in the developing world should be associated with lower fertility and mortality rates there. Calculations are presented that illustrate the effect this negative correlation could have had on the range of the older IS92 emission scenarios, assuming that all other factors are unchanged. Finally, some policy issues concerning population and global warming are reviewed in connection with the IPCC’s omission of population policy discussion in its 1995 Second Assessment Report.