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The strong growth in demand for wood energy in Benin's major cities today represents a real threat to the preservation of forest ecosystems. The promotion of new alternatives such as the use of domestic gas as cooking energy could help to better cope with the adverse effects of climate change resulting from deforestation. The objective of this paper is to analyze the determinants of domestic gas use in Benin. To do so, we used data from 15,000 households collected during the Global Food Vulnerability and Security Analysis Survey of 2017. We then compared the prediction of household gas use determinants by Multilayer Perceptron Neural Networks (MLP) and classical Binary Logistic Regression (BLR). The two approaches have highlighted as important factors of the adoption of Domestic Gas in Benin, the residence department (here department of the Littoral) and the level of education. We also noted that the MLP highlighted more adoption factors than the BLR model (income, ethnicity, and number of wives of the household head). In order to increase the use of domestic gas on a large scale, the Government must put in place a policy that promotes the physical and financial accessibility (through subsidies) of the product to the large mass of the population in our cities which are still dependent on traditional energy sources such as wood fuel and charcoal in order to better protect our forest ecosystems in a sustainable manner. The Government could also strengthen the public-private partnership in this sub-sector by, for example, creating facilities for private economic operators through tax or customs exemption measures.
Bisu DY, Kuhe A, Iortyer HA. Urban household cooking energy choice: an example of Bauchi metropolis, Nigeria. Energ Sustain Soc. 2016;6:15.
Kitoto PAO. Facteurs d’adoption des foyers améliorés en milieux urbains sahéliens camerounais. Développement durable et territories. 2018;9(2):1-20.
Wang Z, Ali S, Akbar A. Rasool F. Determining the Influencing Factors of Biogas Technology Adoption Intention in Pakistan: The Moderating Role of Social Media. Int. J. Environ. Res. Public Health. 2020;17:2311.
Puzzolo E, Zerriffi H, Carter E, Clemens H, Stokes H, Jagger P, et al.Supply considerations for scaling up clean cooking fuels for household energy in low‐ and middle‐income countries. GeoHealth. 2019;3:370–390.
Mbaka CK, Gikonyo J, Kisaka OM. Households’ energy preference and consumption intensity in Kenya. Energ Sustain Soc. 2019;9:20.
Wahyudi J. The determinants factors of biogas technology adoption in cattle farming: Evidences from Pati, Indonesia. International Journal of Renewable Energy Development. 2017; 6(3):235-240.
Stanistreet D, Puzzolo E, Bruce N, Pope D, Rehfuess E. Factors Influencing Household Uptake of Improved Solid Fuel Stoves in Low- and Middle-Income Countries: A Qualitative Systematic Review. Int. J. Environ. Res. Public Health. 2014;11:8228-8250.
Rogers EM. Diffusion of innovations. New York: The Free Press; 2003.
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. 1989;13(3):319-340.
Moore GC, Benbasat I. Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research. 1991;2(3):192-222.
Nan Z, Xunhua G, Guoqing C. IDT-TAM Integrated Model for IT Adoption. Tsinghua Science and Technology. 2008;13(3):306-311.
Cheng S, Cho, V. An integrated model of employees’ behavioral intention toward innovative information and communication technologies in travel agencies. Journal of Hospitality & Tourism Research. 2010;35(4):488-510.
Young K. Cognitive behavior therapy with Internet addicts: Treatment outcomes and implications. Cyber Psychology & Behavior. 2007;10:671–679.
Monge PR, Heiss B, Margolin D. Network evolution in organizational communities. Communication Theory. 2008;18(4):449–477.
Ali-Olubandwa AM, Odero-Wanga D, Kathuri NJ, Shivoga WA. Adoption of Improved Maize Production Practices Among Small Scale Farmers in the Agricultural Reform Era: The Case of Western Province of Kenya. Journal of International Agricultural and Extension Education. 2010;17(1):Spring10.
Feder G, Umali DL. The adoption of agricultural innovations: a review. Technological Forecasting and Social Change. 1993;43(3-4):215-239.
Pohekar SD, Kumar D, Ramachandran M. Dissemination of cooking energy alternatives in India - A review. Renew. Sustain. Energy Rev. 2005;9:379–393.
Quadir SA, Mathur SS, Kandpal TC. Barriers to Dissemination of Renewable Energy Technologies for Cooking. Energy Convers. Manag. 1995;36:1129–1132.
Mittal S, Ahlgren EO, Shukla PR. Barriers to biogas dissemination in India: A review. Energy Policy. 2018;112:361–370.
Danlami AH, Islam R, Applanaidu SD. An Analysis of the Determinants of Households’ Energy Choice: A Search for Conceptual Framework. International Journal of Energy Economics and Policy. 2015;5(1):197-205. ISSN: 2146-4553 Available:www.econjournals.com
Kumar P, Dover RE, Iriarte AD, Rao S, Garakani R, Hadingham S, et al. Aﬀordability, Accessibility, and Awareness in the Adoption of Liqueﬁed Petroleum Gas: A Case-Control Study in Rural India. Sustainability. 2020;12:4790.
Pye A, Ronzi S, Mbatchou Ngahane BH, Puzzolo E, Ashu AH, Pope D. Drivers of the Adoption and Exclusive Use of Clean Fuel for Cooking in Sub-Saharan Africa: Learnings and Policy Considerations from Cameroon. International Journal of Environmental Research and Public Health. 2020;17(16):5874.
Stanistreet D, Hyseni L, Puzzolo E, Higgerson J, Ronzi S, Anderson de Cuevas R, et al. Barriers and Facilitators to the Adoption and Sustained Use of Cleaner Fuels in Southwest Cameroon: Situating ‘Lay’ Knowledge within Evidence-Based Policy and Practice. Int. J. Environ. Res. Public Health. 2019;16:4702.
Pope D, Bruce N, Higgerson J, Hyseni L, Stanistreet D, MBatchou B, et al. Household Determinants of Liquiﬁed Petroleum Gas (LPG) as a Cooking Fuel in SW Cameroon. Eco Health. 2018;15:729–743.
Uhunamure SE, Nethengwe NS, Tinarwo D. Correlating the factors influencing household decisions on adoption and utilisation of biogas technology in South Africa, Renewable and Sustainable Energy Reviews, Elsevier. 2019;107(C):264-273.
Soltani M, Rahmani O, Pour AB, Ghaderpour Y, Ngah I, Misnan SH. Determinants of variation in household energy choice and consumption: Case from Mahabad City, Iran. Sustainability. 2019;11:4775;
Ogwumike FO, Ozughalu UM, Abiona GA. Household energy use and determinants: evidence from Nigeria. International Journal of Energy Economics and Policy. 2014;4(2):248-262. ISSN: 2146-4553, Site web. www.econjournals.com
Makonese T, Ifegbesan AP, Rampedi IT. Household cooking fuel use patterns and determinants across southern Africa: Evidence from the demographic and health survey data. Energy & Environment. 2018;29(1):29–48.
Dewoolkar P, Belhekar V, Bhatkhande A, Hatekar N, Chavan R. Improving adoption of liquefied petroleum gas (LPG) for better health and conservation outcomes. Biodiversity. 2020;21(2):90-96.
Mgimba C, Sanga A, Mwidege A. Why households delay in adopting Liquidified Petroleum Gas fuel for cooking use ? A case of Mbeya City, Tanzania. International Journal of Science, Environment and Technology. 2017;6(3):1963–1971. ISSN 2278-3687 (O), 2277-663X (P).
Rao S, Dahal S, Hadingham S, Kumar P. Dissemination Challenges of Liquefied Petroleum Gas in Rural India: Perspectives from the Field. Sustainability. 2020;12:2327.
Goulda CF, Urpelainenb J. LPG as a clean cooking fuel: Adoption, use, and impact in rural India. Energy Policy. 2018;122:395-408.
Drew PJ, Monson JRT. Artificial neural networks. Surgery. 2000;127:3-11.
Chong AYL. Predicting m-commerce adoption determinants: A neural network approach. Expert Systems with Applications. 2013;40:523–530.
Gregova E, Valaskova K, Adamko P, Tumpach M, Jaros J. Predicting financial distress of Slovak Enterprises: Comparison of selected traditional and learning algorithms methods. Sustainability. 2020;12;3954.
Hajmeera M, Basheerb I. Comparison of logistic regression and neural network-based classiﬁers for bacterial growth. Food Microbiology. 2003;20:43–55.
Schmitt A, Le Blanc B, Corsini MM, Lafond C, Bruzek J. Les réseaux de neurones artiﬁciels - Un outil de traitement de données prometteur pour l’anthropologie. Bulletins et mémoires de la Société d’Anthropologie. Paris. 2001 ;Tome 13, 1-2:143-150.
Czepiel SA. Maximum Likelihood Estimation of Logistic Regression Models: Theory and Implementation.
Hounmenou CG, Tohoun RJ, Gneyou KE, Glèlè-Kakaï R. Empirical determination of optimal conﬁguration for characteristics of a multilayer perceptron neural network in nonlinear regression. Afrika Statistika. 2020;15(3):2413-2429.
Riedmiller M, Braun H. A direct adaptive method for faster back propagation learning: The RPROP algorithm. In H. Ruspini, editor, Proceedings of the IEEE International Conference on Neural Networks. 1993;586-591.
Chen C-K, Hughes J. Using Ordinal regression model to analyze student satisfaction questionnaires. Association for Institutional Research. 2004;1:1-13
Hosmer D, Lemeshow S. Applied logistic regression. 2nd ed. New York: Wiley; 2000.
Bishop C. Neural networks for pattern recognition. Oxford: Oxford University Press; 1995.
Zurada J, Malinowski A, Cloete A. Sensitivity analysis for minimization of input dimension for feedforward neural networks. In: Proc IEEE Int Symp Circuits Systems. 1994;6:447–50.
Olden J, Joy M, Death R. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling. 2004;178:389–397, 11.
Vafeiadis T, Diamantaras KI, Sarigiannidis G, Chatzisavvas KC. A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory. 2015;55:1-9. ISSN 1569-190X.