The 2016-2021 World Outlook for 16 Megawatt Off-Grid Industrial and Commercial Solar Photovoltaic Systems

1.1 Overview 10
1.2 What is Latent Demand and the P.I.E.? 10
1.3 The Methodology 11
1.3.1 Step 1. Product Definition and Data Collection 12
1.3.2 Step 2. Filtering and Smoothing 13
1.3.3 Step 3. Filling in Missing Values 13
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 14
1.3.5 Step 5. Fixed-Parameter Linear Estimation 14
1.3.6 Step 6. Aggregation and Benchmarking 14
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 15
1.4 Frequently Asked Questions (FAQ) 15
1.4.1 Category Definition 15
1.4.2 Units 15
1.4.3 Methodology 17
2.1 The Worldwide Market Potential 18
3.1 Executive Summary 20
3.2 Algeria 22
3.3 Angola 24
3.4 Benin 24
3.5 Botswana 25
3.6 Burkina Faso 26
3.7 Burundi 27
3.8 Cameroon 28
3.9 Cape Verde 29
3.10 Central African Republic 29
3.11 Chad 30
3.12 Comoros 31
3.13 Congo (formerly Zaire) 32
3.14 Cote d'Ivoire 33
3.15 Djibouti 34
3.16 Egypt 35
3.17 Equatorial Guinea 36
3.18 Eritrea 36
3.19 Ethiopia 37
3.20 Gabon 38
3.21 Ghana 38
3.22 Guinea 40
3.23 Guinea-Bissau 40
3.24 Kenya 41
3.25 Lesotho 43
3.26 Liberia 44
3.27 Libya 45
3.28 Madagascar 46
3.29 Malawi 47
3.30 Mali 47
3.31 Mauritania 48
3.32 Mauritius 49
3.33 Morocco 49
3.34 Mozambique 50
3.35 Namibia 51
3.36 Niger 52
3.37 Nigeria 53
3.38 North Sudan 55
3.39 Republic of Congo 56
3.40 Rwanda 57
3.41 Sao Tome E Principe 58
3.42 Senegal 58
3.43 Sierra Leone 59
3.44 Somalia 60
3.45 South Africa 61
3.46 South Sudan 62
3.47 Swaziland 62
3.48 Tanzania 63
3.49 The Gambia 64
3.50 Togo 65
3.51 Tunisia 66
3.52 Uganda 67
3.53 Western Sahara 68
3.54 Zambia 68
3.55 Zimbabwe 69
4.1 Executive Summary 71
4.2 Afghanistan 73
4.3 Armenia 74
4.4 Azerbaijan 75
4.5 Bahrain 76
4.6 Bangladesh 77
4.7 Bhutan 79
4.8 Brunei 79
4.9 Burma 80
4.10 Cambodia 81
4.11 China 81
4.12 Hong Kong 83
4.13 India 84
4.14 Indonesia 86
4.15 Iran 88
4.16 Iraq 91
4.17 Israel 92
4.18 Japan 93
4.19 Jordan 96
4.20 Kuwait 97
4.21 Kyrgyzstan 98
4.22 Laos 99
4.23 Lebanon 100
4.24 Macau 100
4.25 Malaysia 101
4.26 Maldives 102
4.27 Mongolia 103
4.28 Nepal 104
4.29 North Korea 105
4.30 Oman 106
4.31 Pakistan 106
4.32 Palestine 108
4.33 Papua New Guinea 109
4.34 Philippines 110
4.35 Qatar 114
4.36 Saudi Arabia 115
4.37 Seychelles 116
4.38 Singapore 116
4.39 South Korea 117
4.40 Sri Lanka 119
4.41 Syrian Arab Republic 120
4.42 Taiwan 122
4.43 Tajikistan 123
4.44 Thailand 123
4.45 The United Arab Emirates 124
4.46 Timor - Leste, Democratic Republic of 125
4.47 Turkey 125
4.48 Turkmenistan 128
4.49 Uzbekistan 129
4.50 Vietnam 130
4.51 Yemen 131
5 EUROPE 133
5.1 Executive Summary 133
5.2 Albania 135
5.3 Andorra 136
5.4 Austria 136
5.5 Belarus 137
5.6 Belgium 138
5.7 Bosnia and Herzegovina 139
5.8 Bulgaria 140
5.9 Croatia 141
5.10 Cyprus 142
5.11 Czech Republic 143
5.12 Denmark 144
5.13 Estonia 145
5.14 Finland 145
5.15 France 146
5.16 Georgia 148
5.17 Germany 149
5.18 Greece 150
5.19 Hungary 152
5.20 Iceland 153
5.21 Ireland 154
5.22 Italy 155
5.23 Kazakhstan 157
5.24 Kosovo 159
5.25 Latvia 159
5.26 Liechtenstein 160
5.27 Lithuania 161
5.28 Luxembourg 161
5.29 Macedonia 162
5.30 Malta 163
5.31 Moldova 163
5.32 Monaco 164
5.33 Montenegro 165
5.34 Norway 165
5.35 Poland 166
5.36 Portugal 168
5.37 Romania 169
5.38 Russia 170
5.39 San Marino 173
5.40 Serbia 173
5.41 Slovakia 174
5.42 Slovenia 175
5.43 Spain 176
5.44 Sweden 178
5.45 Switzerland 179
5.46 The Netherlands 180
5.47 The United Kingdom 182
5.48 Ukraine 184
6.1 Executive Summary 187
6.2 Argentina 189
6.3 Belize 192
6.4 Bolivia 193
6.5 Brazil 194
6.6 Chile 197
6.7 Colombia 198
6.8 Costa Rica 200
6.9 Ecuador 201
6.10 El Salvador 202
6.11 Guatemala 203
6.12 Guyana 204
6.13 Honduras 204
6.14 Mexico 205
6.15 Nicaragua 207
6.16 Panama 208
6.17 Paraguay 209
6.18 Peru 210
6.19 Suriname 211
6.20 The Falkland Islands 211
6.21 Uruguay 212
6.22 Venezuela 213
7.1 Executive Summary 215
7.2 Antigua and Barbuda 217
7.3 Aruba 217
7.4 Barbados 218
7.5 Bermuda 219
7.6 Canada 219
7.7 Cuba 221
7.8 Dominica 222
7.9 Dominican Republic 223
7.10 Greenland 224
7.11 Grenada 225
7.12 Haiti 225
7.13 Jamaica 226
7.14 Puerto Rico 227
7.15 St. Kitts and Nevis 228
7.16 St. Lucia 228
7.17 St. Vincent and the Grenadines 229
7.18 The Bahamas 230
7.19 The British Virgin Islands 230
7.20 The Cayman Islands 231
7.21 The U.S. Virgin Islands 232
7.22 The United States 232
7.23 Trinidad and Tobago 234
8 OCEANA 235
8.1 Executive Summary 235
8.2 American Samoa 237
8.3 Australia 237
8.4 Christmas Island 238
8.5 Cook Islands 239
8.6 Fiji 240
8.7 French Polynesia 240
8.8 Guam 241
8.9 Kiribati 242
8.10 Marshall Islands 242
8.11 Micronesia Federation 243
8.12 Nauru 244
8.13 New Caledonia 244
8.14 New Zealand 245
8.15 Niue 246
8.16 Norfolk Island 247
8.17 Palau 247
8.18 Solomon Islands 248
8.19 The Northern Mariana Island 249
8.20 Tonga 249
8.21 Tuvalu 250
8.22 Vanuatu 251
8.23 Wallis and Futuna 251
8.24 Western Samoa 252
9.1 Disclaimers & Safe Harbor 253
9.2 ICON Group International, Inc. User Agreement Provisions 254


The concept of latent demand is rather subtle. The term latent typically refers to something that is dormant, not observable, or not yet realized. Demand is the notion of an economic quantity that a target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is commonly defined by economists as the industry earnings of a market when that market becomes accessible and attractive to serve by competing firms. It is a measure, therefore, of potential industry earnings (P.I.E.) or total revenues (not profit) if a market is served in an efficient manner. It is typically expressed as the total revenues potentially extracted by firms. The “market” is defined at a given level in the value chain. There can be latent demand at the retail level, at the wholesale level, the manufacturing level, and the raw materials level (the P.I.E. of higher levels of the value chain being always smaller than the P.I.E. of levels at lower levels of the same value chain, assuming all levels maintain minimum profitability).

The latent demand for 16 megawatt off-grid industrial and commercial solar photovoltaic systems is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be lower either lower or higher than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise from a number of factors, including the lack of international openness, cultural barriers to consumption, regulations, and cartel-like behavior on the part of firms. In general, however, latent demand is typically larger than actual sales in a country market.

For reasons discussed later, this report does not consider the notion of “unit quantities”, only total latent revenues (i.e., a calculation of price times quantity is never made, though one is implied). The units used in this report are U.S. dollars not adjusted for inflation (i.e., the figures incorporate inflationary trends) and not adjusted for future dynamics in exchange rates. If inflation rates or exchange rates vary in a substantial way compared to recent experience, actually sales can also exceed latent demand (when expressed in U.S. dollars, not adjusted for inflation). On the other hand, latent demand can be typically higher than actual sales as there are often distribution inefficiencies that reduce actual sales below the level of latent demand.

As mentioned in the introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved. If fact, all the current products or services on the market can cease to exist in their present form (i.e., at a brand-, R&D specification, or corporate-image level) and all the players can be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will still be an international latent demand for 16 megawatt off-grid industrial and commercial solar photovoltaic systems at the aggregate level. Product and service offering details, and the actual identity of the players involved, while important for certain issues, are relatively unimportant for estimates of latent demand.


In order to estimate the latent demand for 16 megawatt off-grid industrial and commercial solar photovoltaic systems on a worldwide basis, I used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, I heavily rely on the use of certain basic economic assumptions. In particular, there is an assumption governing the shape and type of aggregate latent demand functions. Latent demand functions relate the income of a country, city, state, household, or individual to realized consumption. Latent demand (often realized as consumption when an industry is efficient), at any level of the value chain, takes place if an equilibrium is realized. For firms to serve a market, they must perceive a latent demand and be able to serve that demand at a minimal return. The single most important variable determining consumption, assuming latent demand exists, is income (or other financial resources at higher levels of the value chain). Other factors that can pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), and or changes in utility for the product in question.

Ignoring, for the moment, exogenous shocks and variations in utility across countries, the aggregate relation between income and consumption has been a central theme in economics. The figure below concisely summarizes one aspect of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the average propensity to consume would fall. The average propensity to consume is the level of consumption divided by the level of income, or the slope of the line from the origin to the consumption function. He estimated this relationship empirically and found it to be true in the short-run (mostly based on cross-sectional data). The higher the income, the lower the average propensity to consume. This type of consumption function is labeled "A" in the figure below (note the rather flat slope of the curve). In the 1940s, another macroeconomist, Simon Kuznets, estimated long-run consumption functions which indicated that the marginal propensity to consume was rather constant (using time series data across countries). This type of consumption function is show as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant.

Is it declining or is it constant? A number of other economists, notably Franco Modigliani and Milton Friedman, in the 1950s (and Irving Fisher earlier), explained why the two functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter the time horizon, the more consumption can depend on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to consume is more constant. Similarly, in the long run, households, industries or countries with no income eventually have no consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related and interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for 16 megawatt off-grid industrial and commercial solar photovoltaic systems across some 230 countries. The smallest have fewer than 10,000 inhabitants. I assume that all of these counties fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these countries having wealth, current income dominates the latent demand for 16 megawatt off-grid industrial and commercial solar photovoltaic systems. So, latent demand in the long-run has a zero intercept. However, I allow firms to have different propensities to consume (including being on consumption functions with differing slopes, which can account for differences in industrial organization, and end-user preferences).

Given this overriding philosophy, I will now describe the methodology used to create the latent demand estimates for 16 megawatt off-grid industrial and commercial solar photovoltaic systems. Since ICON Group has asked me to apply this methodology to a large number of categories, the rather academic discussion below is general and can be applied to a wide variety of categories, not just 16 megawatt off-grid industrial and commercial solar photovoltaic systems.

Step 1. Product Definition and Data Collection

Any study of latent demand across countries requires that some standard be established to define “efficiently served”. Having implemented various alternatives and matched these with market outcomes, I have found that the optimal approach is to assume that certain key countries are more likely to be at or near efficiency than others. These countries are given greater weight than others in the estimation of latent demand compared to other countries for which no known data are available. Of the many alternatives, I have found the assumption that the world’s highest aggregate income and highest income-per-capita markets reflect the best standards for “efficiency”. High aggregate income alone is not sufficient (i.e., China has high aggregate income, but low income per capita and can not assumed to be efficient). Aggregate income can be operationalized in a number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or number of households times average household income per capita). Brunei, Nauru, Kuwait, and Lichtenstein are examples of countries with high income per capita, but not assumed to be efficient, given low aggregate level of income (or gross domestic product); these countries have, however, high incomes per capita but may not benefit from the efficiencies derived from economies of scale associated with large economies. Only countries with high income per capita and large aggregate income are assumed efficient. This greatly restricts the pool of countries to those in the OECD (Organization for Economic Cooperation and Development), like the United States, or the United Kingdom (which were earlier than other large OECD economies to liberalize their markets).

The selection of countries is further reduced by the fact that not all countries in the OECD report industry revenues at the category level. Countries that typically have ample data at the aggregate level that meet the efficiency criteria include the United States, the United Kingdom and in some cases France and Germany.

Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, and the World Bank). Depending on original data sources used, the definition of “16 megawatt off-grid industrial and commercial solar photovoltaic systems” is established. In the case of this report, the data were reported at the aggregate level, with no further breakdown or definition. In other words, any potential product or service that might be incorporated within 16 megawatt off-grid industrial and commercial solar photovoltaic systems falls under this category. Public sources rarely report data at the disaggregated level in order to protect private information from individual firms that might dominate a specific product-market. These sources will therefore aggregate across components of a category and report only the aggregate to the public. While private data are certainly available, this report only relies on public data at the aggregate level without reliance on the summation of various category components. In other words, this report does not aggregate a number of components to arrive at the “whole”. Rather, it starts with the “whole”, and estimates the whole for all countries and the world at large (without needing to know the specific parts that went into the whole in the first place).

Given this caveat, in this report we define the sales of "16 megawatt off-grid industrial and commercial solar photovoltaic systems" as including all commonly understood products falling within this broad category, irrespective of product packaging, formulation, size, or form. Companies participating in this industry include BP Solarex, Shell Solar, and Evergreen Solar. All figures are in a common currency (U.S. dollars, millions) and are not adjusted for inflation (i.e., they are current values). Exchange rates used to convert to U.S. dollars are averages for the year in question. Future exchange rates are assumed to be constant in the future at the current level (the average of the year of this publication’s release in 2015).

Step 2. Filtering and Smoothing

Based on the aggregate view of 16 megawatt off-grid industrial and commercial solar photovoltaic systems as defined above, data were then collected for as many similar countries as possible for that same definition, at the same level of the value chain. This generates a convenience sample of countries from which comparable figures are available. If the series in question do not reflect the same accounting period, then adjustments are made. In order to eliminate short-term effects of business cycles, the series are smoothed using an 2 year moving average weighting scheme (longer weighting schemes do not substantially change the results). If data are available for a country, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a country stricken with foot and mouth disease), these observations were dropped or "filtered" from the analysis.

Step 3. Filling in Missing Values

In some cases, data are available for countries on a sporadic basis. In other cases, data from a country may be available for only one year. From a Bayesian perspective, these observations should be given greatest weight in estimating missing years. Assuming that other factors are held constant, the missing years are extrapolated using changes and growth in aggregate national income. Based on the overriding philosophy of a long-run consumption function (defined earlier), countries which have missing data for any given year, are estimated based on historical dynamics of aggregate income for that country.

Step 4. Varying Parameter, Non-linear Estimation

Given the data available from the first three steps, the latent demand in additional countries is estimated using a “varying-parameter cross-sectionally pooled time series model”. Simply stated, the effect of income on latent demand is assumed to be constant across countries unless there is empirical evidence to suggest that this effect varies (i.e., the slope of the income effect is not necessarily same for all countries). This assumption applies across countries along the aggregate consumption function, but also over time (i.e., not all countries are perceived to have the same income growth prospects over time and this effect can vary from country to country as well). Another way of looking at this is to say that latent demand for 16 megawatt off-grid industrial and commercial solar photovoltaic systems is more likely to be similar across countries that have similar characteristics in terms of economic development (i.e., African countries will have similar latent demand structures controlling for the income variation across the pool of African countries).

This approach is useful across countries for which some notion of non-linearity exists in the aggregate cross-country consumption function. For some categories, however, the reader must realize that the numbers will reflect a country’s contribution to global latent demand and may never be realized in the form of local sales. For certain country-category combinations this will result in what at first glance will be odd results. For example, the latent demand for the category “space vehicles” will exist for “Togo” even though they have no space program. The assumption is that if the economies in these countries did not exist, the world aggregate for these categories would be lower. The share attributed to these countries is based on a proportion of their income (however small) being used to consume the category in question (i.e., perhaps via resellers).

Step 5. Fixed-Parameter Linear Estimation

Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the world consists of more than 200 countries, there will always be those countries, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible. For these countries, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a country’s stock of income), but a function of current income (a country’s flow of income). In the long run, if a country has no current income, the latent demand for 16 megawatt off-grid industrial and commercial solar photovoltaic systems is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., countries which earn low levels of income will not use their savings, in the long run, to demand 16 megawatt off-grid industrial and commercial solar photovoltaic systems). In a graphical sense, for low income countries, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income countries are assumed to have a latent demand proportional to their income, based on the country closest to it on the aggregate consumption function.

Step 6. Aggregation and Benchmarking

Based on the models described above, latent demand figures are estimated for all countries of the world, including for the smallest economies. These are then aggregated to get world totals and regional totals. To make the numbers more meaningful, regional and global demand averages are presented. Figures are rounded, so minor inconsistencies may exist across tables.

Step 7. Latent Demand Density: Allocating Across Cities

With the advent of a “borderless world”, cities become a more important criteria in prioritizing markets, as opposed to regions, continents, or countries. This report also covers the world’s top 2000 cities. The purpose is to understand the density of demand within a country and the extent to which a city might be used as a point of distribution within its region. From an economic perspective, however, a city does not represent a population within rigid geographical boundaries. To an economist or strategic planner, a city represents an area of dominant influence over markets in adjacent areas. This influence varies from one industry to another, but also from one period of time to another.

Similar to country-level data, the reader needs to realize that latent demand allocated to a city may or may not represent real sales. For many items, latent demand is clearly observable in sales, as in the case for food or housing items. Consider, again, the category “satellite launch vehicles.” Clearly, there are no launch pads in most cities of the world. However, the core benefit of the vehicles (e.g. telecommunications, etc.) is "consumed" by residents or industries within the world's cities. Without certain cities, in other words, the world market for satellite launch vehicles would be lower for the world in general. One needs to allocate, therefore, a portion of the worldwide economic demand for launch vehicles to regions, countries and cities. This report takes the broader definition and considers, therefore, a city as a part of the global market. I allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its home country, within its region and across the world total. Not all cities are estimated within each country as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same country, a city’s population is not generally used to allocate latent demand. Rather, the level of economic activity of the city vis-à-vis others.
Book features
Format PDF Download
Genre World Outlook Report
Pages 254
  • The 2016-2021 World Outlook for 16 Megawatt Off-Grid Industrial and Commercial Solar Photovoltaic Systems
  • ISBN: 000882875G
  • Please note: ICON Group has a strict no refunds policy.
  • $995

Amazon - Paperback