The Efficiency Structural Trap: Carbon Mix Intensity Dominates Energy Intensity in Fossil-Dependent Systems—Evidence from a Seven-Factor LMDI Decomposition of C？te d’Ivoire (2000-2024)
Studies on energy transition generally treat electrification and energy efficiency as complementary climate levers. In fossil dominated electricity systems, however, electrification and energy efficiency are linked through a common carbon transmission channel, the carbon intensity of electricity generation, which can progressively offset their expected mitigation gains. This study applies a seven-factor LMDI-I decomposition to the energy sector emissions of C？te d’Ivoire over 2000-2024, covering IPCC categories 1.A (fuel combustion) and 1.B (upstream fugitive HC emissions). Data combine the national GHG inventory (IGES, 2000-2022) and ANARE-CI statistics (2023-2024), with inter-source coherence validated at <2%. Emissions increased 5.34-fold over the period (+433.5%). Population growth was the largest contributor (+7662 kt; +38.4%), followed by GDP per capita growth (+7015 kt; +35.2%), carbon mix intensity (+2990 kt; +15.0%), and energy intensity (+2278 kt; +11.4%). The level-2 decomposition disaggregates the aggregate carbon intensity effect into three major carbonisation channels: fossil electrification (+843 kt), transport (+971 kt), and upstream hydrocarbon fugitive emissions (+880 kt), each of which remains embedded within the aggregate carbon intensity term of the conventional Kaya decomposition. To characterise this dynamic, we introduce the Efficiency Structural Trap (EST): the configuration in which carbon mix intensity dominates the energy intensity contribution over the period considered, irrespective of its sign (energy intensity increased by 25.3% over 2000-2024, contributing +2278 kt CO2eq, +11.4% of total emissions growth). The PSE_index stands at 1.31. Granger causality tests on stationary first-difference series reveal no robust temporal precedence (CI → IE: p = 0.42; n.s.), supporting the interpretation of the EST as a strictly descriptive, policy-oriented indicator. Over the study period, the per-unit climate value of energy savings increased by 69.3%: each unit of energy saved in 2024 avoids more emissions than in 2000, because the aggregate carbon intensity of the energy system is higher. At the system level, however, this gain is more than offset by aggregate energy-system carbonisation, which outpaces efficiency improvements by a factor of 1.31. Counterfactual analysis demonstrates that decarbonising the aggregate energy-system carbon intensity (electricity, transport and upstream hydrocarbons combined) constitutes the most effective mitigation lever. Freezing carbon intensity at its 2000 level would
References
[1]
Allan, R.P., Arias, P.A., Berger, S., Canadell, J.G., Cassou, C., Chen, D., Cherchi, A., Connors, S.L., Coppola, E., Cruz, F.A. and Intergovernmental Panel on Climate Change (IPCC) (2023) Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 3-32. https://doi.org/10.1017/9781009157896.001
[2]
Dechamps, P. (2023) The IEA World Energy Outlook 2022—A Brief Analysis and Implications. TheEuropeanEnergyandClimateJournal, 11, 100-103. https://doi.org/10.4337/eecj.2023.03.05
[3]
Pauliuk, S. and Hertwich, E.G. (2015) Socioeconomic Metabolism as Paradigm for Studying the Biophysical Basis of Human Societies. EcologicalEconomics, 119, 83-93. https://doi.org/10.1016/j.ecolecon.2015.08.012
[4]
Bataille, C., ？hman, M., Neuhoff, K., Nilsson, L.J., Fischedick, M., Lechtenb？hmer, S., et al. (2018) A Review of Technology and Policy Deep Decarbonization Pathway Options for Making Energy-Intensive Industry Production Consistent with the Paris Agreement. JournalofCleanerProduction, 187, 960-973. https://doi.org/10.1016/j.jclepro.2018.03.107
[5]
Davis, S.J., Lewis, N.S., Shaner, M., Aggarwal, S., Arent, D., Azevedo, I.L., et al. (2018) Net-Zero Emissions Energy Systems. Science, 360, eaas9793. https://doi.org/10.1126/science.aas9793
[6]
Sorrell, S. (2007) The Rebound Effect: An Assessment of the Evidence for Economy-Wide Energy Savings from Improved Energy Efficiency. UK Energy Research Centre.
[7]
Seto, K.C., Davis, S.J., Mitchell, R.B., Stokes, E.C., Unruh, G. and ürge-Vorsatz, D. (2016) Carbon Lock-In: Types, Causes, and Policy Implications. AnnualReviewofEnvironmentandResources, 41, 425-452. https://doi.org/10.1146/annurev-environ-110615-085934
World Bank (2024) World Development Indicators. World Bank.
[10]
MINETE (2025) Contribution Déterminée au niveau National (CDN 3.0). Ministère de l’Environnement et de la Transition écologique. https://unfccc.int/sites/default/files/2025-10/CDN%203.0%20COTE%20D'IVOIRE.pdf
[11]
Ang, B.W. (2015) LMDI Decomposition Approach: A Guide for Implementation. EnergyPolicy, 86, 233-238. https://doi.org/10.1016/j.enpol.2015.07.007
[12]
Ang, B.W. (2005) The LMDI Approach to Decomposition Analysis: A Practical Guide. EnergyPolicy, 33, 867-871. https://doi.org/10.1016/j.enpol.2003.10.010
[13]
Ang, B.W. and Zhang, F.Q. (2000) A Survey of Index Decomposition Analysis in Energy and Environmental Studies. Energy, 25, 1149-1176. https://doi.org/10.1016/s0360-5442(00)00039-6
[14]
Su, B. and Ang, B.W. (2012) Structural Decomposition Analysis Applied to Energy and Emissions: Some Methodological Developments. EnergyEconomics, 34, 177-188. https://doi.org/10.1016/j.eneco.2011.10.009
[15]
Emodi, N.V., Emodi, C.C., Murthy, G.P. and Emodi, A.S.A. (2017) Energy Policy for Low Carbon Development in Nigeria: A LEAP Model Application. RenewableandSustainableEnergyReviews, 68, 247-261. https://doi.org/10.1016/j.rser.2016.09.118
[16]
Oteng-Abayie, E.F., Asaki, F.A., Duodu, E., Mahawiya, S. and Gyamfi, B.A. (2024) Decomposition Analysis of Electricity Generation on Carbon Dioxide Emissions in Ghana. Heliyon, 10, e28212. https://doi.org/10.1016/j.heliyon.2024.e28212
[17]
Oteng-Abayie, E.F., Dramani, J.B., Adusah-Poku, F., Amanor, K. and Quartey, J.D. (2023) Decomposition and Drivers of Energy Intensity in Ghana. EnergyStrategyReviews, 47, Article ID: 101090. https://doi.org/10.1016/j.esr.2023.101090
[18]
Engo, J. (2018) Decomposing the Decoupling of CO2 Emissions from Economic Growth in Cameroon. EnvironmentalScienceandPollutionResearch, 25, 35451-35463. https://doi.org/10.1007/s11356-018-3511-z
[19]
Inglesi-Lotz, R. (2018) Decomposing the South African CO2 Emissions within a BRICS Countries Context: Signalling Potential Energy Rebound Effects. Energy, 147, 648-654. https://doi.org/10.1016/j.energy.2017.12.150
[20]
Ang, B.W. and Liu, N. (2007) Handling Zero Values in the Logarithmic Mean Divisia Index Decomposition Approach. EnergyPolicy, 35, 238-246. https://doi.org/10.1016/j.enpol.2005.11.001
[21]
Kaya, Y. and Yokobori, K. (1997) Environment, Energy, and Economy: Strategies for Sustainability. United Nations University Press.
[22]
IRENA (2021) Reaching Zero with Renewables: Eliminating CO2 Emissions from Industry and Transport. International Renewable Energy Agency.
[23]
Tapio, P. (2005) Towards a Theory of Decoupling: Degrees of Decoupling in the EU and the Case of Road Traffic in Finland between 1970 and 2001. TransportPolicy, 12, 137-151. https://doi.org/10.1016/j.tranpol.2005.01.001
[24]
Brookes, L. (1990) The Greenhouse Effect: The Fallacies in the Energy Efficiency Solution. EnergyPolicy, 18, 199-201. https://doi.org/10.1016/0301-4215(90)90145-t
[25]
Khazzoom, J.D. (1980) Economic Implications of Mandated Efficiency in Standards for Household Appliances. TheEnergyJournal, 1, 21-40. https://doi.org/10.5547/issn0195-6574-ej-vol1-no4-2
[26]
IRENA (2023) World Energy Transitions Outlook 2023. International Renewable Energy Agency.
[27]
ANARE (2024) Rapport d’Activités. ANARE-CI.
[28]
DGH (2024) Rapport d’activités du secteur amont pétrolier et gazier. Direction Générale des Hydrocarbures, Abidjan, C？te d’Ivoire.
[29]
Granger, C.W.J. (1969) Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica, 37, 424-438. https://doi.org/10.2307/1912791
[30]
Dickey, D.A. and Fuller, W.A. (1979) Distribution of the Estimators for Autoregressive Time Series with a Unit Root. JournaloftheAmericanStatisticalAssociation, 74, 427-431. https://doi.org/10.1080/01621459.1979.10482531
[31]
Granger, C.W.J. and Newbold, P. (1974) Spurious Regressions in Econometrics. JournalofEconometrics, 2, 111-120. https://doi.org/10.1016/0304-4076(74)90034-7
[32]
Liu, Z., Ciais, P., Deng, Z., Lei, R., Davis, S.J., Feng, S., et al. (2020) Near-Real-Time Monitoring of Global CO2 Emissions Reveals the Effects of the COVID-19 Pandemic. NatureCommunications, 11, Article No. 5172. https://doi.org/10.1038/s41467-020-18922-7
[33]
York, R., Rosa, E.A. and Dietz, T. (2003) STIRPAT, IPAT and Impact: Analytic Tools for Unpacking the Driving Forces of Environmental Impacts. EcologicalEconomics, 46, 351-365. https://doi.org/10.1016/s0921-8009(03)00188-5
[34]
Blimpo, M.P. and Cosgrove-Davies, M. (2019) Electricity Access in Sub-Saharan Africa: Uptake, Reliability, and Complementary Factors for Economic Impact. World Bank Publications. https://doi.org/10.1596/978-1-4648-1361-0
[35]
Gielen, D., Boshell, F., Saygin, D., Bazilian, M.D., Wagner, N. and Gorini, R. (2019) The Role of Renewable Energy in the Global Energy Transformation. EnergyStrategyReviews, 24, 38-50. https://doi.org/10.1016/j.esr.2019.01.006
[36]
UNFCCC (2015) The Paris Agreement. United Nations Framework Convention on Climate Change.
[37]
Rogelj, J., Popp, A., Calvin, K.V., Luderer, G., Emmerling, J., Gernaat, D., et al. (2018) Scenarios towards Limiting Global Mean Temperature Increase below 1.5？C. NatureClimateChange, 8, 325-332. https://doi.org/10.1038/s41558-018-0091-3
