




































AGORA International Journal of Economical Sciences, http://univagora.ro/jour/index.php/aijes 

ISSN 2067-3310, E-ISSN 2067-7669 

Vol. 18, No. 1 (2024), pp. 55-64 

 

55 

 

ANALYZING AND FORECASTING CO2 EMISSIONS IN THE 

ALUMINUM SECTOR USING ARIMA MODEL 

 

R. HASANOV, J. SAFAROV, A. SAFARLI 

 

Ramil Hasanov¹, Jamil Safarov², Arzu Safarli³ 

¹ University of Technology of Azerbaijan, Azerbaijan 

https://orcid.org/0000-0003-4267-7039, E-mail: r.hasanov@uteca.edu.az  

² University of Technology of Azerbaijan, Azerbaijan 

https://orcid.org/0009-0004-0392-3860, E-mail: jamil.safarov@Azeraluminium.com   

³ Azerbaijan Technical University, Azerbaijan  

https://orcid.org/0000-0003-2670-0665, E-mail: arzu.safarli@aztu.edu.az  

 

Abstract: The urgent and pressing challenge posed by global climate change 

underscores the critical need for immediate and decisive action, particularly in the mitigation 

of greenhouse gas (GHG) emissions to facilitate a trajectory towards sustainability. The 

significance of the aluminium industry, characterized by its notable carbon footprint, 

accentuates the importance of conducting comprehensive and environmentally conscious 

analyses within this sector. Heightened environmental apprehensions surrounding aluminium 

production necessitate the development of effective approaches for emission forecast. The 

primary aim of the article is to elucidate the present situation and future predictions of carbon 

emissions within the global aluminium industry, particularly in the context of escalating 

concerns regarding global climate security. The research concluded that there will be a 

decrease in CO2 emissions within the aluminium industry in the future as a result. Through 

meticulous assessments and exhaustive forecasts of CO2 emissions across the global 

aluminium industrial chain system, this study employed the Autoregressive Integrated Moving 

Average (ARIMA) model to scrutinize data spanning from 2005 to 2030. By furnishing valuable 

insights into prospective emission patterns and providing guidance for the formulation of 

sustainable policy measures, this research assumes a pivotal role in shaping data-driven 

strategies aimed at mitigating the environmental impact of aluminum production. 

Consequently, it contributes significantly to the collective endeavor to combat climate change 

and foster a more resilient and sustainable future for humanity. 

Keywords: green economics, aluminum industry, CO2 emissions, ARIMA model, 

sustainability. 

 

INTRODUCTION 

Aluminium plays a vital role in various technologies essential for the energy transition, 

but it also serves as a significant source of CO2 emissions, emitting close to 270 million tonnes 

of direct CO2 emissions in 2022, which corresponds to approximately 3% of the world's direct 

industrial CO2 emissions (IEA, 2023). Despite being consumed in lower quantities compared 

to steel or cement, aluminum emerges as the most carbon-intensive material per tonne among 

https://orcid.org/0000-0003-4267-7039
mailto:r.hasanov@uteca.edu.az
https://orcid.org/0009-0004-0392-3860
mailto:jamil.safarov@Azeraluminium.com
https://orcid.org/0000-0003-2670-0665
mailto:arzu.safarli@aztu.edu.az


ANALYZING AND FORECASTING CO2 EMISSIONS IN THE ALUMINUM SECTOR 

USING ARIMA MODEL 

56 

 

the top three highest-emitting materials (Berker, 2022). These statistics reiterate the 

fundamental importance of the current sector in promoting a sustainable eco-economic system. 

Graph 1 depicts the CO2 emissions of the global aluminum industry over the past 18 

years, revealing a notable shift from an upward trend to a decrease since 2020. Concurrently, 

there has been observed growth in global aluminum production, indicating a positive trend 

towards green integration in recent years. 

 

Graph 1. CO2 Emissions In Aluminum Sector (In Million Tons). 

                                
Source: International Aluminum Institution (IAI). https://international-aluminium.org/statistics/greenhouse-gas-

emissions-aluminium-sector/ (25.01.2023) 

 

The global aluminum industry holds considerable economic significance, yet its 

manufacturing activities contribute significantly to greenhouse gas emissions, particularly 

carbon dioxide (CO2). Therefore, understanding the emission patterns within this sector is 

crucial for implementing effective mitigation strategies. Utilizing some techniques in time 

series analysis and forecasting, this research aims to examine CO2 emission data in the global 

aluminum industry to identify prevailing trends and forecast future emission patterns. By 

conducting thorough analysis and forecasting modeling, the findings of this study aim to 

provide insights that can guide strategic decision-making processes geared towards promoting 

sustainability in the aluminum sector. 

 

LITERATURE REVIEW 

Time series forecasting is indispensable across multiple fields, including finance, 

economics, engineering, and social sciences. ARIMA (Autoregressive Integrated Moving 

Average) models serve as a cornerstone in statistical time series analysis, offering a flexible 

and powerful framework for forecasting future values based on past observations. ARIMA 

models have become a well-established tool within the field of economics for forecasting key 

macroeconomic indicators. These indicators, such as Gross Domestic Product (GDP), inflation 

rates, and unemployment levels, provide crucial insights into the health and trajectory of a 

nation's economy. Vafin (2020) utilized the Automatic ARIMA forecasting method to 

anticipate significant macroeconomic indicators across seven notable economies, uncovering 

anticipated reductions in employment and inflation within the United States, alongside 

diminishing rates of labor force participation in Russia, alongside other forecasted patterns. 

https://international-aluminium.org/statistics/greenhouse-gas-emissions-aluminium-sector/
https://international-aluminium.org/statistics/greenhouse-gas-emissions-aluminium-sector/


Ramil HASANOV, Jamil SAFAROV, Arzu SAFARLI 

57 

 

Moreover, this model is extensively employed in financial analysis and forecasting. Numerous 

scientific articles exist that explore this area (Li, Han, & Song, 2020; Ariyo, Adewumi, & Ayo, 

2014; Cheng et al, 2020).  

In addition to these instances, the ARIMA forecasting model is employed in a multitude 

of scholarly investigations centered on sustainable economic development and resilience. 

Using annual time series data from 1960 to 2017 on CO2 emissions in China, employs Nyoni, 

& Mutongi (2019) the Box-Jenkins ARIMA approach to model and forecast CO2 levels, 

revealing an anticipated increase in emissions and suggesting four key policy recommendations 

for the Chinese government. Dritsaki, M., & Dritsaki, C. (2020) investigate the most effective 

model for forecasting CO2 emissions in the EU-28, utilizing an ARIMA(1,1,1)-ARCH(1) 

model with parameter optimization via maximum likelihood estimation, employing both static 

and dynamic methods for forecasting, with the static approach demonstrating superior accuracy 

in forecasting.  

The thorough scientific analysis of carbon dioxide emissions within the global 

aluminum industry, renowned for its expansive industrial network, holds considerable 

importance on a global scale. Ciacci et al. (2014) employs Standard Material Flow Analysis 

(MFA) and Life Cycle Assessment (LCA) models to study the historical greenhouse gas 

emissions from Italian aluminum production (1960-2009), aiming to guide future industrial 

and environmental policies. It calculates annual emissions and cradle-to-gate factors, revealing 

the carbon footprint and highlighting emissions transfers between production and use locations. 

The study suggests potential emissions reductions through aluminum recycling and 

underscores the value of integrating MFA and LCA for comprehensive environmental analysis. 

Liu & Muller (2012) conducted an extensive analysis of aluminum life cycle assessments, 

taking into account sustainability principles from a wide-ranging perspective. 

 

DATA AND METHODOLOGY 

The present study employs data concerning Aluminum Life Cycle Emissions (ALCE), 

offering insights into carbon CO2 emissions associated with the worldwide aluminum sector. 

Spanning from 2005 to 2022, this dataset provides a holistic perspective on emission patterns 

throughout the specified timeframe. Sourced primarily from the International Aluminium 

Institute (IAI, 2023) and derived through Total-Cradle to Gate calculations, this dataset serves 

as a pivotal resource for comprehending the environmental ramifications of aluminum 

production processes, serving as the cornerstone for subsequent analyses and interpretations 

within the study. 

Table 1 presents the descriptive statistical analysis of CO2 emissions in the aluminum 

sector spanning from 2005 to 2022, providing essential insights for environmental and 

industrial evaluation. The mean annual emission is documented at 913.00 million tons, with a 

median emission of 964.00 million tons, signifying the central tendency of the data. 

Additionally, the dataset highlights a range of emissions, with a minimum of 569.00 million 

tons and a maximum of 1133.00 million tons, alongside a moderate level of variability (SD = 

189.25), a slight left skew (skewness = -0.35), and a moderately peaked distribution (kurtosis 

= 1.74). These findings are instrumental for researchers and policymakers to discern trends and 

devise sustainable strategies within the aluminum industry. 



ANALYZING AND FORECASTING CO2 EMISSIONS IN THE ALUMINUM SECTOR 

USING ARIMA MODEL 

58 

 

Table 1. Descriptive Statistical View (ALCE In Level). 

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis 

913.00 964.00 1133.00 569.00 189.25 -0.35 1.74 

 Unit root tests are statistical techniques employed to ascertain the stationarity of a time 

series variable. A stationary time series variable is characterized by consistent attributes, 

including a constant mean, variance, and autocorrelation, which remain unchanged over time. 

The Augmented Dickey-Fuller (ADF) test (1979) evaluates the presence of a unit root, 

implying non-stationarity, in a time series variable, whereas the Phillips-Perron (PP) test 

(1988), a comparable unit root examination, incorporates modifications to account for serial 

correlation and heteroscedasticity. ADF and PP tests are implemented to evaluate the 

stationarity of the ALCE series. In instances where the data exhibits non-stationarity, indicating 

alterations in statistical properties over time, differencing techniques are employed to attain 

stationarity. The correlogram provides a visual representation of the autocorrelation function 

(ACF) and partial autocorrelation function (PACF) of the ALCE series. This visualization 

facilitates the identification of serial dependence patterns, indicating correlations between past 

and present values within the dataset. 

The Autoregressive Integrated Moving Average (ARIMA) model serves as a widely 

employed approach for time series forecasting. This model was established through the 

pioneering contributions of Box and Jenkins (1970). Their incorporation of differencing into 

the ARMA framework transformed the ARIMA model, enabling the handling of non-stationary 

time series data via observation differencing. ARIMA model is defined by three parameters, 

represented as (p, d, q), where p signifies the count of autoregressive terms integrating 

preceding values, d indicates the degree of differencing essential for achieving stationarity, and 

q denotes the number of moving average terms accounting for past forecast errors.  

In this research endeavor, we examine the utilization of this methodology for forecasting 

emissions, as articulated by the equation: 

                                         ALCEt = α₁ ALCEt
1 + εt                                                           

(1) 

here: 

 ALCEt is the CO2 emissions in aluminum sector in period t. 

 ALCEt
1 is CO2 emissions in the aluminum sector during the preceding period. 

 εt represents the error term, assumed to be white noise, characterized by being 

independent and identically distributed with a mean of zero. 

The selection of the most suitable ARIMA model relies on statistical metrics such as p-

values, R-squared, Akaike Information Criterion (AIC), and Schwarz Bayesian Criterion 

(SBC), which penalize excessive model complexity in favor of simpler alternatives 

demonstrating enhanced forecasting precision.  

For a more dependable forecasting analysis of the model, it is imperative to obtain the 

AR and MA roots results. These outcomes offer critical information about the stability and 

characteristics of the autoregressive (AR) and moving average (MA) components, thereby 

augmenting the accuracy and reliability of the forecasting process. 



Ramil HASANOV, Jamil SAFAROV, Arzu SAFARLI 

59 

 

 RESULTS 

Table 2 displays the results from two unit root examinations, specifically ADF and PP 

tests, which are conducted assuming the presence of both trend and intercept in the data. The 

p-values presented in the table serve the purpose of determining whether the null hypothesis of 

a unit root can be rejected. Upon the first difference operation, where the data undergoes 

differencing once, the test statistic yields a value of -3.262 with a corresponding p-value of 

0.107. Despite this p-value being lower compared to the initial level, it does not attain sufficient 

significance to reject the null hypothesis at the conventional 5% significance level. However, 

its proximity to the threshold suggests the consideration of either a test with improved power 

or a decreased significance level. Conversely, at the second difference, the test statistic stands 

at -4.718 with a p-value of 0.010, denoting statistical significance. This finding implies the 

potential attainment of stationarity in ALCE subsequent to two difference operations. The 

outcomes of the Phillips-Perron test closely mirror those of the ADF test, displaying 

insignificance at the initial level and first difference, but significance at the second difference, 

hinting at the likelihood of achieving stationarity after two differences. 

 

Table 1. Unit Root Test: ALCE. 

Augmented Dickey-Fuller Level 1st Difference 2nd Difference 

Trend and 

Intercept 

t-statistic -1.075    -3.262   -4.718 

p-value 0.903     0.107    0.010 

Phillips-Perron Level 1st Difference 2nd Difference 

Trend and 

Intercept 

t-statistic -1.308    -3.284    -6.586 

p-value 0.849     0.104     0.000 

 

The correlogram provided in Graph 2 illustrates the autocorrelation coefficients for 

ALCE across various lag intervals, spanning up to 12 lags, where the vertical axis denotes these 

coefficients and the horizontal axis represents lag duration. Additionally, the correlogram 

incorporates partial autocorrelation coefficients (PAC), which reveal correlation patterns after 

adjusting for intervening lags. Notably, the prominently elevated and positive first lag 

autocorrelation coefficient indicates a persistent influence of CO2 emissions, while diminishing 

coefficients suggest decreasing correlations with extended lag durations. Multiple coefficients, 

demonstrating statistical significance, suggest the presence of serial dependence, while other 

observed patterns hint at potential non-stationarity. Leveraging the correlogram facilitates the 

identification of appropriate ARIMA models for diagnostic purposes. 



ANALYZING AND FORECASTING CO2 EMISSIONS IN THE ALUMINUM SECTOR 

USING ARIMA MODEL 

60 

 

Graph 2. The Correlogram Of ALCE. 

*Derived through the utilization of the Eviews software application 

 

Several scholars (Ning, Pei, & Li, 2021; Sharma et al., 2023; Lotfalipour, Falahi, & 

Bastam, 2013) have applied the ARIMA forecasting model to analyze carbon dioxide (CO2) 

emissions, as evidenced by a body of research in the field. Based on the findings presented in 

Table 4, we examine the suitability of employing AR(1)MA(1) models for the analysis and 

forecasting of time series data. The AR(1) model characterizes a time series wherein the current 

value exhibits a linear dependence on the previous value (lag 1), while the MA(1) model 

captures the relationship between the current value and the preceding error term (lag 1). In 

contrast, AR(1) employs lagged outcome values for forecasting, while MA(1) utilizes 

unobserved error terms as inputs, leading to distinct methodologies and estimation outcomes. 

Through comparison and diagnostic evaluation, encompassing measures such as R-squared and 

Hannan-Quinn criteria, it is evident that the AR(1)MA(1) model proves to be more suitable. 

We conduct estimation for the equation below to identify a prospective model suitable 

for forecasting, ultimately culminating in the generation of forecasts: 

                        ALCE = C(1) + AR(1) * C(2) + MA(1) * C(3) + UNCOND 

(2) 

ALCE refers to carbon emissions in the aluminum sector, with C(1), C(2), and C(3) 

denoting the coefficients associated with the constant, autoregressive (AR), and moving 

average (MA) components, respectively. UNCOND signifies unconditional estimation. 

 

Table 3. ARMA Maximum Likelihood Method Results. 

 AR(1)MA(1) AR(1)MA(2) AR(2)MA(1) 

R-squared 0.948 0.933 0.930 

F-statistic 85.232 65.107 62.826 

Akaike criter. 10.965 11.198 11.226 

Schwarz criter. 11.163 11.396 11.424 

Hannan-Quinn criter. 10.992 11.226 11.253 

 

Graph 3 illustrates the roots of the AR and MA polynomials within the ARIMA model 

framework. The positioning of AR roots is crucial for determining the stability of the AR 

Date: 03/27/24   Time: 14:09

Sample: 2005 2022

Included observations: 18

Autocorrelation Partial Correlation AC  PAC  Q-Stat  Prob

1 0.837 0.837 14.840 0.000

2 0.671 -0.100 24.969 0.000

3 0.537 0.009 31.888 0.000

4 0.397 -0.112 35.937 0.000

5 0.210 -0.252 37.153 0.000

6 0.042 -0.085 37.206 0.000

7 -0.101 -0.099 37.539 0.000

8 -0.212 -0.032 39.150 0.000

9 -0.297 -0.030 42.688 0.000

10 -0.363 -0.067 48.608 0.000

11 -0.417 -0.104 57.572 0.000

12 -0.437 -0.037 69.033 0.000



Ramil HASANOV, Jamil SAFAROV, Arzu SAFARLI 

61 

 

component, which is confirmed when all roots lie within the unit circle. Similarly, the 

placement of MA roots plays a significant role in model stability, with invertibility being 

essential for precise estimation and interpretation. The stability of the ARIMA model is 

essential for analyzing ALCE data, emphasizing the importance of thorough validation of 

model assumptions and meticulous interpretation of results to support informed decision-

making in environmental science and policy domains. 

 

Graph 3. ALCE: Inverse Roots of AR/MA Polynomial(s). 

                                                   
   *Derived through the utilization of the Eviews software application. 

 

The graphical depiction in Graph 4 presents historical ALCE data and projections from 

2006 to 2030, with actual emissions depicted by the blue line and forecasted emissions by the 

orange line (ALCEF), alongside uncertainty boundaries indicated by the green (LB) and red 

(UP) lines. Historically, ALCE demonstrated a consistent upward trajectory until around 2022, 

indicative of industrial influences. However, post-2022 forecasts anticipate a decline in ALCE, 

potentially influenced by environmental regulations and technological advancements. It is 

forecasted that if the trajectory persists according to the present trend, by 2030, notwithstanding 

the augmentation in yearly production and consumption, there will be a 6% decrease in CO2 

emissions compared to the levels observed in 2022. 

 

Graph 4. ALCE: Forecasting. 

      
*Derived through the utilization of the Eviews software application. 

 

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1 0 1

AR roots

MA roots

ALCE: Inverse Roots of AR/MA Polynomial(s)



ANALYZING AND FORECASTING CO2 EMISSIONS IN THE ALUMINUM SECTOR 

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62 

 

The presence of uncertainty bounds emphasizes the need for cautious policymaking and 

adaptive measures within the industry. These projections have implications for environmental 

sustainability, policy development, and industrial strategies, underscoring the importance of 

vigilance and adaptation in achieving emission reduction goals within the aluminum sector. 

 

DISCUSSION 

Mathematical forecasting offers a means to approximate future outcomes based on 

current trends, although exact figures may not always be attainable within this approach. 

Furthermore, various factors beyond mathematical models can significantly impact the socio-

economic landscape. Based on the CRU report, there is a forecasted surge in demand for 

aluminum, projected to rise by 40% by the year 2030 (Alfed, 2022).  

In the absence of significant technological advancements, the anticipated surge in 

aluminum demand is expected to necessitate a substantial boost in production, consequently 

leading to a parallel rise in carbon emissions. The majority of carbon emissions are typically 

released during the energy production phase. The life-cycle greenhouse gas emissions linked 

to one metric ton of primary aluminum are estimated to be approximately 14.7 tons of CO2-

equivalent (Peng, Ou, Yan, & Wang, 2019). Transitioning to renewable energy sources such as 

solar, wind, and hydroelectric power is essential for curtailing CO2 emissions in the global 

aluminum industry. This change not only lessens the carbon footprint associated with energy-

intensive processes but also fosters sustainability and environmental stewardship within the 

sector. By investing in renewable energy technologies and embracing eco-friendly practices, 

aluminum producers can make significant strides in combatting climate change and promoting 

a greener future for the planet.  

Another potential scenario to consider is the implementation of inert anode technology 

during the aluminum production process. The adoption of inert anode technology represents a 

substantial stride in mitigating carbon waste during the electrolysis phase, consequently 

elevating the sustainability credentials of aluminum manufacturing. This breakthrough not only 

reduces environmental footprint but also enhances resource utilization and operational 

efficiency across the sector (Hasanov, 2023). The future sustainability of the aluminum industry 

hinges on two key elements: the evolution of the renewable energy sector and the integration 

of innovative technologies. These factors are instrumental in guiding the industry toward 

greater environmental responsibility, facilitating reductions in carbon emissions, and fostering 

a more sustainable approach to aluminum production. 

 

CONCLUSIONS 

This research utilized ARIMA models to examine and forecast CO2 emissions within 

the aluminum sector. Through unit root tests, it was determined that two differencing operations 

were necessary to achieve data stationarity. Analysis of the correlogram helped identify 

appropriate ARIMA models, with the AR(1)MA(1) model selected based on diagnostic 

evaluation criteria. The estimated ARIMA model effectively captured historical CO2 emission 

trends and projected emissions up to 2030, revealing an anticipated decrease in emissions after 

2022, likely driven by environmental regulations and advancements in technology. Following 

a thorough diagnostic process, the AR(1)MA(1) model emerged as the most suitable for 



Ramil HASANOV, Jamil SAFAROV, Arzu SAFARLI 

63 

 

forecasting CO2 emissions. This model attained an R-squared value of 0.948, indicating its 

capability to explain nearly 95% of the variability in the CO2 emission data. Additionally, the 

Hannan-Quinn criterion, a metric assessing model simplicity, favored the AR(1)MA(1) model 

over alternative specifications. The forecast analysis results indicate that if the current trend 

persists, there will be a 6% reduction in the volume of CO2 emitted from the global aluminum 

sector by the year 2030.  

 

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