Assessing the effect of green financing on inclusive growth: Evidence from Ghana
Description
This paper examines whether green financing creates inclusive growth in Ghana. Quantitative time series data spanning 1990 to 2020 were gathered from secondary sources. Secondary data gathered from World development index, UNDP, UNEP, and IEA were used to establish the link between green financing and inclusive growth in Ghana. CO2 emissions per capita and renewable energy as percentage of total primary energy were used as proxies for green financing whilst human development index, education and life expectancy were used as proxies for inclusive growth. The ARDL techniques were adopted to analyse the data. The study finds that clean energy, CO2 emission reductions and education do not create inclusive growth in Ghana in the short-run. Improvement in the human development index and life expectancy creates inclusive growth both in the short and long run. The study demonstrates that education without appropriate skills and employment avenues would not reduce poverty and spurs on inclusive growth. Purposive sampling approach and desk survey method were adopted to gather the data from the World Bank, United Nations, UNDP, IEA, OECD, IMF, GSS, GLSS, and Ghana Multidimensional Poverty documents for the analysis. Explanatory and descriptive techniques were applied to arrive at the conclusions based on the data collected. To situate this study in context, the human development model was used to link the concepts and variables to arrive at a clear conclusion. This econometric model adopted is convenient for any data size (Odhiambo, 2009). Vector auto regression technique is used and the Granger causality model is applied based on the error-correction mechanisms. The paper used ARDL regression and bound test analysis to assess the interconnection between green financing indicators and inclusive growth. The ARDL regression model works by using one or more independent variables to predict the impacts on dependent variable (Kumari & Yadav, 2018). The connection between dependent variable and one or more independent variables were assessed, and since this paper tested for the impact of green finance indicators on inclusive growth, the use of ARDL regression is fit and proper. The econometric model adopted was: INCGROWTH = β0 +β1CO2EM + β2CLENRG + β3EDU + β4LEXP + β5HDI + εi Methodologically, the estimation focus is on how changes in the green financing might affect inclusive growth in Ghana. It is necessary to expatiate the property of time series variables in the ARDL model to determine how well they work with the preferred estimating method before looking at the results. This is usually done using the Unit Root Test.
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The data were gathered from credible international and local sources from Ghana and from institutions which have credibility in data collection, storage and analysis. These include the World Bank database, World development index, UNDP, UNEP, International Energy Agency, Ghana Statistical Service, OECD. The data were gathered from the websites of these institutions using desk survey method. The data were compared from similar data from other sources for credibility and consistency before they are adopted. They were analysed using ARDL regression models and Augmented Dickey-Fuller bound test for the analysis