Forecasting of CO2 emissions from fuel combustion using trend analysis

Köne, Aylin Çiğdem; Büke, Tayfun

doi:10.1016/j.rser.2010.06.006

Cited by 227 publications

(78 citation statements)

References 25 publications

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“…Those pathways remaining within the lower budget required an immediate and rapid decline in Annex 1 emissions and an early peak in non-Annex 1 emissions, unless the latter's emission growth was constrained to rates much lower than historical trends (i.e. to 1%, compared with current growth of 3-4% per year [48]). In all cases, Annex 1 emissions continue to decline following the current economic downturn at rates in excess of 5 per cent per year.…”

Section: Phil Trans R Soc a (2011)mentioning

confidence: 99%

“…25 Bottom-up and built on typically idealized inputs with only limited regard for 'real-world' constraints. 26 While Köne & Büke's [48] paper was published after many of the scenarios referred to, the overarching data and trend lines underpinning Köne and Büke's analysis were available at the time many of the scenarios were developed. 27 The inclusion of bio-CCS also demonstrates a degree of non-contextual engagement with technology.…”

Section: (D) Simple and Complex Scenariosmentioning

confidence: 99%

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Beyond ‘dangerous’ climate change: emission scenarios for a new world

Anderson

Bows

2011

Phil. Trans. R. Soc. A.

340

180

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The Copenhagen Accord reiterates the international community’s commitment to ‘hold the increase in global temperature below 2 degrees Celsius’. Yet its preferred focus on global emission peak dates and longer-term reduction targets, without recourse to cumulative emission budgets, belies seriously the scale and scope of mitigation necessary to meet such a commitment. Moreover, the pivotal importance of emissions from non-Annex 1 nations in shaping available space for Annex 1 emission pathways received, and continues to receive, little attention. Building on previous studies, this paper uses a cumulative emissions framing, broken down to Annex 1 and non-Annex 1 nations, to understand the implications of rapid emission growth in nations such as China and India, for mitigation rates elsewhere. The analysis suggests that despite high-level statements to the contrary, there is now little to no chance of maintaining the global mean surface temperature at or below 2 ° C. Moreover, the impacts associated with 2 ° C have been revised upwards, sufficiently so that 2 ° C now more appropriately represents the threshold between ‘dangerous’ and ‘extremely dangerous’ climate change. Ultimately, the science of climate change allied with the emission scenarios for Annex 1 and non-Annex 1 nations suggests a radically different framing of the mitigation and adaptation challenge from that accompanying many other analyses, particularly those directly informing policy.

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Section: Phil Trans R Soc a (2011)mentioning

confidence: 99%

Section: (D) Simple and Complex Scenariosmentioning

confidence: 99%

Beyond ‘dangerous’ climate change: emission scenarios for a new world

Anderson

Bows

2011

Phil. Trans. R. Soc. A.

340

180

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“…Linear regression model [7], time series model [32], Grey (1,1) forecasting model [33] and Grey Verhulst model [34] are selected to generate the individual forecasting results. The reason why we choose these models is that they have been widely and successfully used in forecasting CO 2 emissions.…”

Section: Experimental Simulationmentioning

confidence: 99%

“…Meng [5] adopted a logistic function to simulate emissions from fossil fuel combustion. Bulent [6], Köne [7] and Raghuvanshi [8] employed trend analysis approaches for modeling World total CO 2 emissions and CO 2 emissions from power generation in India. Liang [9] established a multi-regional input-output model for energy requirements and CO 2 emissions for eight economic regions in China and performed scenario studies for the years 2010 and 2020.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

Hong

Sun

2013

Energies

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Abstract:The accurate forecasting of carbon dioxide (CO 2 ) emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β) arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn't consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE) objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO 2 emission of the World top-5 CO 2 emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE) and mean absolute error (MAE) are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree.

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“…As the social economy develops, the electricity consumption share in end-use energies generally increases gradually. As a result, the annual electricity consumption trend is usually steeper than the total energy consumption and CO 2 emissions trends [17]. In the period of economic boom, the electricity consumption trend will be very steep and the growth rate of it may increase gradually.…”

Section: Introductionmentioning

confidence: 99%

A Small‐Sample Adaptive Hybrid Model for Annual Electricity Consumption Forecasting

Meng¹,

Fu²,

Shi³

et al. 2017

Mathematical Problems in Engineering

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Annual electricity consumption forecasting is one of the important foundations of power system planning. Considering that the long-term electricity consumption curves of developing countries usually present approximately exponential growth trends and linear and accelerated growth rate trends may also appear in certain periods, this paper first proposes a small-sample adaptive hybrid model (AHM) to extrapolate the above curves. The iterative trend extrapolation equation of the proposed model can simulate the linear, exponential, and steep trends adaptively at the same time. To estimate the equation parameters using small samples, the partial least squares (PLS) and iteration starting point optimization algorithms are suggested. To evaluate forecasting performance, the artificial neural network (ANN), grey model (GM), and AHM are used to forecast electricity consumption in China from 1991 to 2014, and then the results of these models are compared. Analysis of the forecasting results shows that the AHM can overcome stochastic changes and respond quickly to changes in the main electricity consumption trend because of its specialized equation structure. Overall error analysis indicators also show that AHM often obtains more precise forecasting results than the other two models.

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Forecasting of CO2 emissions from fuel combustion using trend analysis

Cited by 227 publications

References 25 publications

Beyond ‘dangerous’ climate change: emission scenarios for a new world

Beyond ‘dangerous’ climate change: emission scenarios for a new world

Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

A Small‐Sample Adaptive Hybrid Model for Annual Electricity Consumption Forecasting

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