Forecasting Greenhouse Gas Emissions
and Sustainable Growth in Montenegro:
a SVAR Approach

Muhadinović, Milica; Djurovic, Gordana; Bojaj, Martin M.

doi:10.15244/pjoes/132625

Cited by 2 publications

(2 citation statements)

References 40 publications

(45 reference statements)

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“…[11] predicted CO2 emissions in the G7 countries. [12] utilized a recursive structural vector autoregression method to forecast GHGs in Montenegro. [13] constructed a novel multivariable grey forecasting model based on the smooth generation of independent variable sequences with variable weights and new multivariable grey prediction model with structure compatibility for forecasting of CO2 with the effect of renewable energy in Turkey.…”

Section: Literature Overviewmentioning

confidence: 99%

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Estimations of Green House Gases Emissions of Turkey by Statistical Methods

ÖZTÜRK,

EMİR

2024

KONJES

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The way of life, consumption habits, urbanization rate, type of energy production and increasing energy need with growing economies and population progressively promote the GHGs emissions to Earth’s atmosphere. GHGs consisting of CH4, N2O, CO2, H2O and HFCs cause the climate change, disrupting ecological balance, melting glaciers with global warming in the last decades. Therefore, the issues of future prediction and reduction of GHGs emissions became crucial for policy makers of Turkey and other countries under the international protocols and agreements. This article aims to present the prediction and 8-year future forecasting of CH4, N2O and CO2 emissions of Turkey using past annual data between years 1970 and 2018 with grey, autoregressive integrated moving average and double exponential smoothing models. Based on the results, the best prediction performance is reached by DES model followed by ARIMA and GM for all the emissions. MAPEs calculated from the available data and prediction by DES model from 1970 to 2018 are 0.285, 0.355 and 0.408 for CH4, N2O and CO2 in turn. DES future estimations of CH4, N2O and CO2 at 2026 year are determined as 50700 kiloton of CO2 eq., 38100 thousand metric ton of CO2 eq., and 512000 kilotons.

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Section: Literature Overviewmentioning

confidence: 99%

“…For this reason, the countries led by the G7 have increased their GHGs forecasting studies related to the decrease of energy consumption [11]. Moreover, the usage of renewable energy types as wind, solar, wave, waste recycle and combustion systems emitting less GHGs are to be recommended for energy production [12,13].…”

Section: Introductionmentioning

confidence: 99%

Estimations of Green House Gases Emissions of Turkey by Statistical Methods

ÖZTÜRK,

EMİR

2024

KONJES

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Ukraine’s Green Economy Growth in the Context of Industry 4.0: Challenges and Solutions

Korohodova,

Moiseienko,

Hlushchenko

et al. 2024

ECO

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The research highlights the importance of prioritizing new sources of economic growth for the world, with a specific focus on Ukraine. The information base of the study included data from the statistical and managerial reporting of enterprises, the State Statistics Service of Ukraine, and information-analytical materials from international organizations. The authors analyze scientific works associated with the European Green Deal (EGD) to identify historic periods of green economy growth and establish growth trends. The authors argue that favorable conditions are necessary to achieve sustainable green growth within the framework of the EGD. The study aims to introduce an improved economic definition, namely the efficiency of green (natural) capital, aligned with the provisions of the EGD in the context of Industry 4.0. The analysis demonstrated that the coefficient of green (natural) capital (GNC) efficiency is crucial in decision-making processes by enterprises, taking into account ecological and green (natural and resource) factors. The analysis also includes a flowchart of Ukraine‘s green economy growth in the environment of Industry 4.0 as of 2022. The research results have led to the improvement of scientific and methodological approaches to assessing the eco-economic efficiency of advanced green technologies. The introduced green (natural) capital indicator allowed to display of the theory of achieving more products and services with fewer resources, including natural and ecological ones.

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Forecasting Greenhouse Gas Emissions and Sustainable Growth in Montenegro: a SVAR Approach

Abstract: This paper uses a recursive structural vector autoregression method to investigate and forecast the linkage and causality between greenhouse gas emissions (GHG) and Gross Domestic Product (GDP)

Cited by 2 publications

References 40 publications

Estimations of Green House Gases Emissions of Turkey by Statistical Methods

Estimations of Green House Gases Emissions of Turkey by Statistical Methods

Ukraine’s Green Economy Growth in the Context of Industry 4.0: Challenges and Solutions

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