Abstract
In this study, we investigate the path toward cleaner generation systems based on the forecast of renewable production diffusion in developing and developed countries. We analyze the factors that affect investments in RES generation and are able to explain the mid-term diffusion of renewable energy sources. This empirical analysis is performed on a large dataset of 129 countries and 32 variables that are observed during 1995–2011. Because of the large number of variables and their high degree of collinearity, the first step of the analysis was implementing a Dynamic Factor Analysis to extract factors that explain the majority of the variation of the original variables. In the second step, we determine the key factors that promote RES investments by using a panel regression model. Then, model estimates are used to determine out-of sample predictions. The results of the empirical analysis are separated into developed and developing countries according to the World Bank income classification. All the countries increase their share of RES generation in the next year but with different growth rates. Developing countries invest less than developed countries and prefer traditional generation sources. In developing countries, investments are enhanced by international financial aid. Conversely, developed countries demonstrate greater environmental awareness and, in many cases, incentivize the diffusion of green generation. However, certain developed countries prefer to invest less in renewable energies because they are tied to an economic system that is based on fossil fuels.
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Notes
The list of countries and ISO3 codes are provided in Appendix A.
Due to their poor representation, the results for the developing countries in the first and the second income-groups are not reported in graphic-form, but only in terms of the quartile distribution of the forecast for RES growth rate in the follow-up of the paper.
On February 22, 2017, wind power supplied 104% of the country’s energy needs for each day.
Countries to be eligible for the ODA recipients list must have low-income and face severe structural impediments to sustainable development.
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A Appendix
A Appendix
Countries ISO codes
Low income | Lower middle income | Upper middle income | High income | ||||
|---|---|---|---|---|---|---|---|
Country | ISO code | Country | ISO code | Country | ISO code | Country | ISO code |
Benin | BEN | Armenia | ARM | Albania | ALB | Argentina | ARG |
Burundi | BDI | Bangladesh | BGD | Algeria | DZA | Australia | AUS |
Cambodia | KHM | Bolivia | BOL | Belize | BLZ | Austria | AUT |
Central African Republic | CAF | Cameroon | CMR | Botswana | BWA | Bahrain | BHR |
Congo, Dem. Rep. | COD | Congo, Rep. | COG | Brazil | BRA | Barbados | BRB |
Gambia, The | GMB | Côte d’Ivoire | CIV | Bulgaria | BGR | Belgium | BEL |
Haiti | HTI | Egypt, Arab Rep. | EGY | China | CHN | Canada | CAN |
Malawi | MWI | El Salvador | SLV | Colombia | COL | Chile | CHL |
Mali | MLI | Ghana | GHA | Costa Rica | CRI | Croatia | HRV |
Mozambique | MOZ | Guatemala | GTM | Dominica | DMA | Cyprus | CYP |
Nepal | NPL | Guyana | GUY | Ecuador | ECU | Czech Republic | CZE |
Niger | NER | Honduras | HND | Fiji | FJI | Denmark | DNK |
Sierra Leone | SLE | India | IND | Gabon | GAB | Estonia | EST |
Tanzania | TZA | Indonesia | IDN | Iran, Islamic Rep. | IRN | Finland | FIN |
Togo | TGO | Kenya | KEN | Iraq | IRQ | France | FRA |
Uganda | UGA | Kyrgyz Republic | KGZ | Jamaica | JAM | Germany | DEU |
Lao PDR | LAO | Jordan | JOR | Greece | GRC | ||
Lesotho | LSO | Kazakhstan | KAZ | Hungary | HUN | ||
Mauritania | MRT | Libya | LBY | Iceland | ISL | ||
Moldova | MDA | Malaysia | MYS | Ireland | IRL | ||
Morocco | MAR | Maldives | MDV | Israel | ISR | ||
Myanmar | MMR | Mauritius | MUS | Italy | ITA | ||
Low income | Lower middle income | Upper middle income | High income | ||||
|---|---|---|---|---|---|---|---|
Country | ISO code | Country | ISO code | Country | ISO code | Country | ISO code |
Nicaragua | NIC | Mexico | MEX | Japan | JPN | ||
Pakistan | PAK | Mongolia | MNG | Korea, Rep. | KOR | ||
Papua New Guinea | PNG | Namibia | NAM | Kuwait | KWT | ||
Philippines | PHL | Panama | PAN | Latvia | LVA | ||
Senegal | SEN | Paraguay | PRY | Lithuania | LTU | ||
Sri Lanka | LKA | Peru | PER | Luxembourg | LUX | ||
Swaziland | SWZ | Romania | ROU | Malta | MLT | ||
Syrian Arab Republic | SYR | Thailand | THA | Netherlands | NLD | ||
Tajikistan | TJK | Tunisia | TUN | New Zealand | NZL | ||
Ukraine | UKR | Turkey | TUR | Norway | NOR | ||
Vietnam | VNM | Poland | POL | ||||
Yemen, Rep. | YEM | Portugal | PRT | ||||
Zambia | ZMB | Russian Federation | RUS | ||||
Slovak Republic | SVK | ||||||
Slovenia | SVN | ||||||
Spain | ESP | ||||||
Sweden | SWE | ||||||
Switzerland | CHE | ||||||
Trinidad and Tobago | TTO | ||||||
United Arab Emirates | ARE | ||||||
United Kingdom | GBR | ||||||
United States | USA | ||||||
Uruguay | URY | ||||||
Venezuela, RB | VEN | ||||||
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Romano, A.A., Scandurra, G., Carfora, A. et al. Forecasting the RES generation in developed and developing countries: a dynamic factor model approach. Energy Syst 10, 1071–1091 (2019). https://doi.org/10.1007/s12667-018-0297-5
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DOI: https://doi.org/10.1007/s12667-018-0297-5