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Wind energy is seen as an important energy to sustainably meet the energy needs of Ghana. However, the industry in Ghana is yet to take off due to policy uncertainty and regulatory costs. The paper analyzed the key determinants and how they interact to impact the scaling up of wind energy in Ghana, using time series data, the vector auto regression (VAR) model from 2013 to 2019.There were four endogenous variables, grouped under policy, population growth, wind capacity, and electrification rate. The findings revealed the dynamic behavior of the variables from the VAR to a strongly significant positive correlation to deploying wind energy in Ghana. The impulse response functions (IRFs) equally exhibited a positive impact long-run trajectory growth of the variables after a shock to the system. The response of the first lags had differences of log policy and that of the log of GDP produced a curious result from the shock by taking a steady positive growth path in the short run and nosedived to a negative pathway in the long run. On the other hand, the interaction of the first differences of the lags of log wind capacity and log policy is quite instructive, as the headwind produced a negative relationship in the short run and to a positive growth path in the long run. This was anticipated, as the wind capacity installation of Ghana is expected to increase in the long run, when pipeline projects materialize.

Wind energy continues to make inroads in Africa due to falling costs and technological advancements. Most African countries are planning, exsiccating and connecting their renewable energy projects with national grid system with giving high propriety to energy security, sustainable energy consumption and low carbon emission. Many policies have been enacted by countries to promote the scaling up of wind energy and renewable energy in particular, across the globe. However, these policies have mixed effects on the deployment of wind energy. For this purpose, current study used panel data and fixed effects model for 17 African countries with wind installed generation capacity to determine the driver of wind energy development on the African continent between 2008 and 2017. The variables were grouped into three thematic areas: policy, socioeconomic, and country-specific factors. After conducting the analysis, socioeconomic variables (GDP, CO 2 , energy use) and energy security variables (energy import, electricity consumption) have significant effects in determining the scaling up of wind energy in Africa. However, the policy variables of FITs, licensing during, and Tax did not have significant effects on wind energy capacity addition for the case of Africa. This study adds to the drivers of nascent wind energy deployment literature in Africa. This study suggests that set of effecitive policies are deem necessary to scale up wind energy in Africa. KeywordsRenewable energy . Wind energy . Electricity . CO 2 . Scaling up Nomenclature RETS renewable energy technologies FITs feed in tariffs GDP gross domestic product CO2 carbon dioxide PIDA African Union's Programme for Infrastructure

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