Mod\u00e9lisation de l\u2019\u00e9volution des anomalies de temp\u00e9rature globale et des Gaz \u00e0 effet de serre \u00e0 l’aide des mod\u00e8les ARIMA et ARDL<\/a><\/span><\/h3>\n\n\nDr. Claude Ahouangninou, Ma\u00eetre de Recherche, IIRDD Canada<\/h4>\n\n\nR\u00e9sum\u00e9<\/h4>\n\n\n\n\n\n\n
Ce travail de recherche s\u2019inscrit dans une perspective d\u2019analyse quantitative du changement climatique, en \u00e9tudiant la dynamique des anomalies de temp\u00e9rature globale et des gaz \u00e0 effet de serre (GES), notamment le dioxyde de carbone (CO\u2082), le m\u00e9thane (CH\u2084) et le protoxyde d\u2019azote (N\u2082O). L\u2019objectif principal est de mod\u00e9liser ces s\u00e9ries temporelles \u00e0 l\u2019aide des mod\u00e8les ARIMA et ARDL, et d\u2019identifier d\u2019\u00e9ventuelles relations de long terme entre les \u00e9missions de GES et le r\u00e9chauffement climatique.
L\u2019\u00e9tude repose sur des donn\u00e9es climatiques annuelles issues de sources officielles (NOAA, NASA GISS), couvrant des p\u00e9riodes variables selon les s\u00e9ries. Les tests de stationnarit\u00e9 indiquent que les anomalies de temp\u00e9rature sont int\u00e9gr\u00e9es d\u2019ordre 1, tandis que le CO\u2082, le CH\u2084 et le N\u2082O sont int\u00e9gr\u00e9s d\u2019ordre 2. Des mod\u00e8les ARIMA ont \u00e9t\u00e9 estim\u00e9s pour chaque s\u00e9rie, avec des performances pr\u00e9dictives satisfaisantes. Les tests sur les r\u00e9sidus ont confirm\u00e9 la validit\u00e9 des mod\u00e8les (absence d\u2019autocorr\u00e9lation, pas de volatilit\u00e9 conditionnelle, normalit\u00e9).
L\u2019approche ARDL a permis d\u2019\u00e9tudier l\u2019effet conjoint du CO\u2082 et du CH\u2084 sur les anomalies de temp\u00e9rature. Le mod\u00e8le retenu, un ARDL(1,0,0), pr\u00e9sente des coefficients significatifs pour le CH\u2084 (p < 0,001) et le CO\u2082 (p < 0,01), avec un R\u00b2 ajust\u00e9 de 0,845. Le test de coint\u00e9gration de Pesaran confirme une relation de long terme (F = 11,34 ; p < 0,001). Les tests diagnostiques indiquent l\u2019absence d\u2019autocorr\u00e9lation, d\u2019h\u00e9t\u00e9rosc\u00e9dasticit\u00e9 conditionnelle (ARCH) et une distribution normale des r\u00e9sidus. \u00c0 l\u2019inverse, les mod\u00e8les de type GARCH, VaR ou EVT ont \u00e9t\u00e9 \u00e9cart\u00e9s, les s\u00e9ries ne pr\u00e9sentant pas de comportement instable ou extr\u00eame.
Ces r\u00e9sultats mettent en \u00e9vidence le r\u00f4le structurant des concentrations de GES, en particulier du CO\u2082, dans l\u2019\u00e9volution des anomalies de temp\u00e9rature. Ils soutiennent la n\u00e9cessit\u00e9 de politiques climatiques ambitieuses et fond\u00e9es sur des analyses empiriques solides. Ce travail ouvre la voie \u00e0 des recherches futures mobilisant des donn\u00e9es \u00e0 fr\u00e9quence plus \u00e9lev\u00e9e, des facteurs explicatifs suppl\u00e9mentaires, ou des approches multivari\u00e9es dynamiques.
<\/p>\n\n\n
Mots-cl\u00e9s : changement climatique, anomalies de temp\u00e9rature, GES, ARIMA, ARDL, coint\u00e9gration
<\/p>\n\n\n\n\n\n\n
Abstract<\/h4>\n\n\n\n\n\n\n
This research adopts a quantitative approach to climate change analysis, focusing on the dynamics of global temperature anomalies and greenhouse gases (GHGs), notably carbon dioxide (CO\u2082), methane (CH\u2084), and nitrous oxide (N\u2082O). The main objective is to model these time series using ARIMA and ARDL models, and to identify possible long-term relationships between GHG emissions and global warming.\nThe study is based on annual climate data from official sources (NOAA, NASA GISS), covering different periods depending on the variable. Stationarity tests indicate that temperature anomalies are integrated of order 1, while CO\u2082, CH\u2084, and N\u2082O are integrated of order 2. ARIMA models were estimated for each series, showing satisfactory predictive performance. Residual analysis confirmed the statistical validity of the models (no autocorrelation, no conditional heteroskedasticity, normally distributed residuals).\nThe ARDL approach was used to analyze the joint effect of CO\u2082 and CH\u2084 on temperature anomalies. The selected model, an ARDL(1,0,0), shows significant coefficients for CH\u2084 (p < 0.001) and CO\u2082 (p < 0.01), with an adjusted R\u00b2 of 0.845. The Pesaran bounds test confirms the existence of a long-term relationship (F = 11.34; p < 0.001). Diagnostic tests indicate no residual autocorrelation, no ARCH effects, and normal residual distribution. Conversely, GARCH, VaR, and EVT models were excluded, as the series showed no unstable or extreme behavior.\nThese results highlight the structural role of GHG concentrations, particularly CO\u2082, in driving temperature anomalies. They support the implementation of ambitious climate policies grounded in robust statistical modeling. The study also opens perspectives for future research using higher-frequency data, additional explanatory variables, or multivariate dynamic models.\n<\/p>\n\n
Keywords: climate change, temperature anomalies, GHG, ARIMA, ARDL, cointegration
Pour citer ce rapport:
<\/strong>Ahouangninou, C. (2025). Mod\u00e9lisation de l’\u00e9volution des anomalies de temp\u00e9rature globale et des Gaz \u00e0 effet de serre (GES) \u00e0 l’aide des mod\u00e8les ARIMA et ARDL. Institut International de Recherche pour le D\u00e9veloppement Durable, IIRDD Press, Qu\u00e9bec, Canada, 94p. https:\/\/doi.org\/10.5281\/zenodo.17729809<\/p>\n\n\n\n\n\nVous pouvez t\u00e9l\u00e9charger ce rapport de recherche ici\u00a0<\/a><\/strong><\/span>
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