metadata: authors: 0: author: Unnada Chewpreecha
1: author: Dennig,Francis
2: author: Hansen,Ib
count: Guinea-Bissau
docna: 0: docna: FTT-FLEX : Flexible Technology Diffusion
            Analysis Tool for Data Poor Countries
docty: Policy Research Working Paper
abstracts: cdata!: AbstractTo achieve substantial emission
            reductions, widespread low-carbon technology adoption is
            vital. The process by which new technologies are developed
            and adopted and how their costs evolve is critical to
            understanding decarbonization. Modeling of this process
            requires a tool that realistically describes several
            phenomena related to technology adoption in different
            sectors, including technology diffusion, investment
            decisions, evolution of technology costs, and technology
            lock-in, among others. This paper introduces FTT-FLEX, a
            simplification of the Future Technology Transformation
            model.  FTT-FLEX is suitable for application as a
            single-country, standalone tool or in connection with a
            macroeconomic model. FTT-FLEX captures the core
            country-level of features of Future Technology
            Transformation (knowledge spillovers as the driver of
            technology cost and inertia in the adoption of new
            technologies) as they pertain to an individual country and
            greatly reduces the data required as compared with the
            global Future Technology Transformation model. As presented,
            FTT-FLEX is a natural complement to country-specific
            macroeconomic models that analyze the decarbonization of key
            emitting sectors in small developing countries. The utility
            of FTT-FLEX is demonstrated by a decarbonization analysis
            for the power sector in Guinea-Bissau.
display_title: FTT-FLEX : Flexible Technology
            Diffusion Analysis Tool for Data Poor Countries
listing_relative_url: /research/2024/05/34315484/ftt-flex-flexible-technology-diffusion-analysis-tool-data-poor-countries
topicv3: Renewable Energy Policy and Regulation
            m1326171 526,Power Sector Economics m1326387 516,Innovation and Technology Policy m1327692 378,Macroeconomic Modelling and Statistics
            m1504530 3108,MFMod m1504542 3120
publishtoextweb_dt: 2024-05-08T00:00:00Z
dois: 10.1596/1813-9450-10767
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 29
author_keys: 0: author_key: NA
1: author_key: 2061445
2: author_key: 440135
model_extractions: Policy Research Working Paper 10767

# FTT-FLEX

Flexible Technology Diffusion Analysis Tool for Data Poor Countries _Unnada Chewpreecha Francis Dennig Ib Hansen_ Macroeconomics, Trade and Investment Global Practice May 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10767

# **Abstract**

To achieve substantial emission reductions, widespread low-carbon technology adoption is vital. The process by which new technologies are developed and adopted and how their costs evolve is critical to understanding decarbonization. Modeling of this process requires a tool that realistically describes several phenomena related to technology adoption in different sectors, including technology diffusion, investment decisions, evolution of technology costs, and technology lock-in, among others. This paper introduces FTT-FLEX, a simplification of the Future Technology Transformation model. FTT-FLEX is suitable for application as a single-country, standalone tool or in connection with a macroeconomic model. FTT-FLEX captures the core country-level of features of Future Technology Transformation (knowledge spillovers as the driver of technology cost and inertia in the adoption of new technologies) as they pertain to an individual country and greatly reduces the data required as compared with the global Future Technology Transformation model. As presented, FTT-FLEX is a natural complement to country-specific macroeconomic models that analyze the decarbonization of key emitting sectors in small developing countries. The utility of FTTFLEX is demonstrated by a decarbonization analysis for the power sector in Guinea-Bissau.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at uchewpreecha@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **FTT-FLEX: Flexible Technology Diffusion Analysis Tool for Data Poor Countries**

_Unnada Chewpreecha, Francis Dennig and Ib Hansen_<sup>_*_</sup> JEL No.: O33; O38; E17; Q55; Q42; Q43; Q48 Keywords: technology diffusion model, low carbon policies, macroeconomic model

> * Special thanks to Andrew Burns, Charl Jooste, Joseph Pryor, Rafael Ferreira, and Jean-Francois Mercure for their valuable review inputs and insightful comments. Any remaining errors and viewpoints are solely those of the authors. The authors also acknowledge the generous funding provided by the multi-donor Climate Support Facility for this research.
# **1. Introduction and objective**

In their pursuit of a sustainable future, and given the urgent need to address climate change, countries worldwide are actively pursuing decarbonization strategies (United Nations, 2015). These strategies require the formulation of policies that facilitate the reduction of carbon emissions in the day-to-day operations of an economy, including in the power sector. An effective policy must take into consideration economic incentives but should also consider the complex dynamics by which low-carbon technologies are adopted to meet increasing power demand.

Data-rich high-income countries often have access to analytical tools to evaluate low-carbon policies and their implications for emission reductions, energy uses, government budgets, and overall macroeconomic impacts. However, many developing countries face significant hurdles, due in part to limited data availability. While the World Bank’s MFMod (Burns et al, 2019) and MANAGE (Beyene et al, 2023) models lend themselves to analyzing the economic incentives and consequences of climate change policies, they lack features related to how the diffusion of new technologies might endogenously affect the cost and therefore uptake of different technologies.

This paper introduces FTT-FLEX, a model designed to bridge that gap in a developing country context. Applied to the power sector, FTT-FLEX can be used to help design policies to decarbonize the power sector in a small open economy context. FTT-FLEX can also be used in smaller high-income countries whose actions are sufficiently small on a global scale as not to affect global prices significantly.

# **2. The FTT-FLEX model**

FTT-FLEX is a simplification of on an already established global modeling framework of technology diffusion called Future Technology Transformation-FTT (Mercure, 2012). The framework is applicable to the supply of different goods for which there is a menu of technologies including energy, transport, steel, or heating (Mercure, 2012; Mercure et al., 2014; Mercure et al., 2018; Knobloch et al., 2018, Vercoulen et al., 2018).

This paper focuses on the application of FTT-FLEX in the supply of electrical power. However, the approach laid out can be extended to the choice of technology in other sectors as well. FTT-FLEX reduces the data requirements of the global FTT model, allowing it to be used in countries with limited data. It focusses on the process embedded in the Global FTT model by which small developing economies adopt and diffuse power generation technologies available at the global level but incorporates the impact that external decisions might have on the global technology-cost frontier faced by the smaller economy.

In FTT, the technology diffusion process of a given technology depends both on its cost structure and the extent to which it is already used (its share in total generation capacity) both at the global and local level. Technology cost is assumed to be a declining function of global cumulative past investments in the technology. Experience with a new technology, proxied by its share in total output, constitutes a global knowledge externality. As the world installs more capacity in emergent technologies the cost of these technologies for new investors comes down. In FTT-FLEX, the reduction in costs from the diffusion and installation of technologies in the rest of the world are an exogenous input. Nevertheless, domestic diffusion is affected (as in the larger FTT model) by the extent to which different technologies are used in the domestic economy.

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In contrast, standard “least-cost” energy systems models propose an efficient path in which the menu of cheapest technologies is chosen to satisfy a given path for energy demand – but those costs are not affected by the extent of take-up of a technology. In FTT, lower cost technologies also have a pro tanto advantage. But increasing market share generates an incumbency advantage that can either reinforce or mute the cost advantages. A technology with plenty of installed capacity in the domestic market will have several advantages over an incipient technology that are unrelated to costs. For instance, investors having seen the technology applied elsewhere will exhibit greater confidence in established technologies, reducing uncertainty around projects and perception of risks. This introduces hysteresis into the evolution of costs and the diffusion of technologies, helping to explain why older well-established technologies may persist even though new technologies may have lower costs.

FTT-FLEX can be used alone or in combination with a macro-economic model. Used alone, FTT-FLEX for the power sector takes as an exogenous input an electricity demand path and models the composition of technologies that are chosen over time to meet that demand. The user can modify parameters to implement most policies typically considered by governments in their decarbonization strategies (carbon tax, energy tax, subsidies, kick-start policies, and regulations), and evaluate the implications of those policies on the trajectories of cost, investment, technology mix, and emissions.

When used in conjunction with a macroeconomic model, the electricity demand path is determined by the macroeconomic model (as a function of prices, growth and other economic factors) and passed to FTT-FLEX, which determines the electricity price and power sector investment pathways that are then passed back to the macroeconomic model. Allowing electricity demand to adjust to prices and vice versa, improves the realism of the combined model as compared to the partial-equilibrium outputs derived from a least-cost model or FTT-FLEX operating in isolation.

Section 3, below, describes in more detail the intuition and main features of FTT-FLEX. It is followed by section 4, which describes how FTT-FLEX can be coupled with a macroeconomic model (in this instance, the World Bank’s Macro-Fiscal Model for Climate Analytics (MFMod-CC)). Finally, section 5 presents a case study that illustrates some of the advantages of establishing the economic impacts of transition policies by jointly modeling power demand, technological diffusion, and macroeconomics.

While this paper focuses on the use of FTT-FLEX in the analysis of the decarbonization of the power sector, it can also be applied to other problems (such as heating and transportation), where future costs are likely to depend on the extent to which new technologies have been adopted.

# **3. FTT-FLEX features (power sector application)**

The bulk of this section describes FTT-FLEX as applied to the power sector. It concludes with a short overview of alternative applications. The complete list of model equations is provided in Annex I at the end of the paper.

# **3.1 Diffusion and share dynamics**

FTT-FLEX is a dynamic model that calculates the technological distribution in the supply of electrical power as a function of an electricity demand path provided to it. In any given period, electricity, a flow, is provided by the installed stock of generation capacity and various intermediate inputs (coal, oil, sun, wind, and hydro). Different feasible technologies are embodied in this capital stock, so that the total electricity supply is distributed into shares corresponding to the technologies used. As the capital stock 3
1+ exp ~~(e~~ <u>Cost</u> ~~)~~
<!-- Start of picture text --> Consumer choices '<br>'<br>> xj!<br>o 1<br><<<br>$<br>u52 F(x) .<br>; SN<br>Generalised cost x<br>>:<br>2 ' People adopting i<br>im 9 '<br>5 ' F(x) = 1- Fji(Ax)<br>ire 1<br>2$f<br>3 ' Oi) -= [52\/97 + OF2<br>3 1<br>E People adopting j '<br>rs} pting J H<br>Generalised cost difference Ax<br><!-- End of picture text --> <!-- Start of picture text --> Consumer choices<br>> vj ty<br>i=o q 1<br>g 1 1<br>rm i1 1'<br>a:<br>Consumer choices People adopting j<br>><br>°o<br>i=<br>o<br>Ss<br>s<br>uw<br>U<br>People adopting i F<br>Generalised cost difference A x<br><!-- End of picture text -->
Given the distribution of technologies, we can derive the dynamic of technological diffusion. Let be the share of technology _i_ in period _t_ (the sum over all technologies in each period is equal to 1), then the change in the share of technology tt _i_ over time _t_ ( ) is given by: ii SS tt ii ∆SS � ⋅�NN tt tt−1 tt−1 tt−1 tt−1 tt−1 tt−1 ii ii jj iijj iijj jjii jjii Focusing on the term in brackets, we can see that technology ∆SS = SS jj=1 SS �AA FF _i_ grows more quickly when its diffusion − AA FF rates and probabilities of lower cost ( ) are high in the pairwise comparisons with the other technologies. Growth in the use of technology _i_ is penalized when the reverse comparisons are high iijj iijj AA FF ( ). An essential feature of the model is the AAjjiiFFiijj term outside the sum. It captures the incumbency advantage discussed in the introduction. Say there are two technologies, tt−1 _COAL_ and _SOLAR,_ for which the ii SS cost and diffusion comparisons (the entire term inside the sum) are, for the sake of argument, identical. From the perspective of a standard least cost model the technologies would be equivalent. But if the historical process has resulted in the current share of _COAL_ being significantly greater than that of _SOLAR,_ ( C C ), then the incumbency advantage will tilt new investments towards coal and there will be much more new tt tt _COAL_ capacity than _SOLAR_ capacity in the immediate future _._ This SSC CCSS SS ≫SS incumbency advantage captures network effects in the supply chains, such as the existence of technology-specific skilled labor, and the non-pecuniary influence of familiarity on the risk perceptions of investors that new technologies cannot overcome immediately, even if the new technology has lower “average costs”.

This incumbency advantage imposes friction on the adjustment process, with the important implication that the usual equivalence between price- and quantity-based policies is broken. In a frictionless model (such as a least cost optimization model), a carbon price policy and a policy of technology share mandates that achieve the same energy mix will have the same cost. In FTT the carbon price needed to overcome the friction _and_ achieve the same energy mix as the direct mandate would be significantly higher. By the same token, policies that accelerate the transition in the direction of new low-cost technologies – by forcing increases in their share through demonstration projects or plant lifetime regulation mandates (which would speed the retirement of old technologies)– generate significant value for the economy by increasing their share in the installed capital stock, and thereby speeding up the transition toward them.<sup>2</sup> In practice, quantity and price policies target distinct market failures in the technology diffusion process—network effects and carbon emissions, respectively. By utilizing both policies in tandem, the deployment of low carbon technologies can be synergistically accelerated.

**3.2 Cost dynamics** Another important feature of FTT-FLEX relates to the dynamics of average costs �������� (the average cost of technology _i_ ). This variable measures the levelized cost of electricity (LCOE) for technology _i._ This can CCc cc_ıı be split into the unit cost by factor: capital cost, operations and maintenance cost (O&M), fuel cost, and carbon cost. As briefly discussed in the introduction, FTT and FTT-FLEX implement a _learning curve_ for

> 2 In a global model (such as FTT-power) such policies have a further effect of bringing down the average cost through the cumulative global supply channel, and thus generate distinct long run equilibria. However, this feature is not present in FTT-FLEX. Since FTT-FLEX is specifically designed for small emitters and data-poor countries, their technology profiles are unlikely to significantly impact global outcomes.

6
the capital and O&M cost components. This learning curve translates into a fixed percentage cost reduction for each percent increase in global cumulative installed capacity of a technology, a relationship known as Wright’s law. In practice, for solar energy it is assumed that for every percent increase in global cumulative production there is a 0.25% reduction in cost, in line with historical trends (IRENA 2023).

The learning curve feature allows assumptions about global decarbonization trajectories to be easily converted into domestic cost trajectories. If the world decarbonizes more quickly, solar in the modeled country will become more competitive with time, making a domestic solar-based net zero policy cheaper. Conversely, a slower global expansion of solar would have an adverse effect on the domestic cost of decarbonization.

# **3.3 Reduced data requirements**

In addition to implementing the technology diffusion process described above, FTT-FLEX substantially reduces the data requirements as compared with FTT or other global energy systems models. Table 1 summarizes data requirements in FTT-FLEX (power sector).

_Table 1: Summary of FTT-FLEX data requirement_

|**Country specific data requirement**|**Country specific policy variables**<br>**(default = zero)**|**Global data assumptions (provided)**|
|---|---|---|
|Starting share of power generation by<br>technology|Carbon tax rate and carbon tax rate<br>exemptions by fuel|LCOE (means and standard deviation)<br>by technology broken down to<br>•<br>Capital<br>•<br>O&M<br>•<br>Fuel<br>(Assumed 7% discount rate and zero<br>carbon tax). Source: IEA|
|Total electricity demand projections|Fuel tax by fuel-type|CO2intensity by power generation<br>technology.<br>Source: IEA|
||Investment subsidy by technology|Global baseline cumulative power<br>production by technology<br>Source: Our World In Data (history)<br>and IEA three future scenarios, World<br>EnergyOutlook 2022|
||Minimum and maximum generation<br>potential by technology (default 0 Gwh<br>and 3000 Gwh)|Lifetime of different kinds of power<br>plant (years)<br>Source: IEA|
|||Lead construction time of different<br>power plant types (years)<br>Source: IEA|
|||Learning rate by technology<br>Source: Main FTT|
|||International fuel price assumptions<br>based on IEA three future scenarios.<br>Source: World Energy Outlook 2022,<br>IEA|

FTT-FLEX utilizes technology-specific cost data from the IEA's Projected Costs of Generating Electricity (2020) database, which includes capital, operating and maintenance, and fuel costs. Average costs and 7
standard deviations are calculated from this database. The default assumptions in the cost data include a 7% discount rate and zero carbon tax rate. The model also incorporates other global assumptions such as average lifetime and lead construction time of different power generation technologies (IEA), CO2 emission coefficient per GWh (IEA), learning rate for renewable technologies (IRENA), and international energy price (IEA). While these global data sets are provided as defaults, users have the flexibility to revise them or use alternative data sources according to their preferences.

Regarding cumulative global power production, the model implements projections from the IEA. Users can select from three scenarios for cumulative global power production and future fossil fuel prices, sourced from the IEA’s World Energy Outlook (2022): the Stated Policies Scenario (STEPS), Announced Pledges Scenario (APS), and Net Zero Emissions (NZE). According to the IEA, these scenarios are linked to global temperature increases of approximately 2.5 ̊C, 1.7 ̊C, and 1.5 ̊C, respectively. Coupled with the learning rate assumptions from IRENA, the global production data in these scenarios are sufficient to describe the dynamics of each technology’s average LCOE in the FTT-FLEX model.

**Where available, domestically sourced technology cost data are preferred to the global estimates,** as these sources tend to better reflect local attributes like labor costs, logistic expenses, and production costs. In cases where a country intends to import equipment and external capacity, utilizing a global cost database becomes an acceptable option. The FTT-FLEX database offers pre-collected global average cost data, serving as an initial reference point for a readily accessible tool. In the next version of FTT-FLEX, users will be able to set their own discount rate to reflect local financing conditions or connect the discount rate to interest rates in a macroeconomic model (see the Discussion section).

In summary, the country specific data required to run FTT-FLEX are:

- Historical data for the production shares of each technology

- Total electricity demand projections

- Production maxima for physically limited technologies like hydroelectricity, wind, and solar

Since the future share dynamics depend on the shares today (this is an important feature of the model dynamics), the historical production shares by technology are the main country specific initial condition of setting up the model.

For some technologies geography places a hard physical limit on the potential for domestic capacity. This is most clearly the case for hydroelectric power, for which potential sites are easily enumerated, but it is also a factor for wind and solar, for which the scope for adding capacity is not boundless. If such data can be obtained, the model implements a cost penalty to the technology as its specific capacity limit is approached.

In the absence of more detailed data, the dynamics of the underlying diffusion theory does most of the heavy lifting. The analysis and conclusions that can be drawn are thus strongly contingent on how appropriate the theory is to the domestic setting (see Limitations section in this paper). The robust empirical support that has been found for these dynamics in Mercure et al., 2021, Mercure et al., 2018b and Semieniuk et al., 2022 in the context of the FTT model suggests that incorporating these dynamics even in low-data contexts is likely preferable to the alternative of assuming static costs.

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# **Box 1: FTT-FLEX and FTT-Power comparison**

The full FTT-Power model, on which FTT-FLEX is based on, implements additional features with greater data requirements.

FTT-Power implements dispatch and storage modules, which account for the cost of intermittency of new renewable energy capacity (cloudy days or days with now wind). This requires significant detail on the distributions of load and demand.

Instead of the capacity limit that we implement for certain technologies, FTT-Power implements natural resource cost curves, whereby the quality of suitable sites declines with the amount of locally installed capacity, thus reducing the energy yield of additional installations.

Both of these features could be integrated into FTT-FLEX but require large amounts of data that may be difficult to acquire in a developing country context.

Table below summarizes the features of the full FTT model and those of FTT-FLEX.

|**Model features**|**FTT**|**FTT-FLEX**|
|---|---|---|
|Technologydiffusion theory|||
|Global learningrate||based on exogenousglobal cumulativeproduction|
|Breakdown of LCOEs|||
|Global resource cost curve||X simpler treatment for diminishing return to renewables<br>investment and exogenous cap on maximum and minimum<br>generation|
|Dispatching submodule (Power)||X (this could be added where relevant without increasing data<br>requirements but at the expense of an additional complexity.)|
|Many policyinstruments|||

# **3.4 FTT-FLEX model relationships**

FTT-FLEX incorporates the core FTT preference, diffusion rate, and share equations outlined in Section 3. While retaining the essence of the main FTT equations, other equations within FTT-FLEX have undergone simplification by eliminating explicit references to predefined FTT regions, technologies, or sectors. In this study, we have employed FTT-FLEX within the context of the power sector, and several equations have been tailored to this sector's unique characteristics.

9
_Table 2: Summary of FTT-FLEX equations_

||**FTT FLEX-Equations**|
|---|---|
|**Technology-specific equation**||
|Core FTT equations|•<br>Investor preferences<br>•<br>Diffusion rate<br>•<br>Technologyshares dynamic|
|Learning rate|•<br>Investment learning rate (global exogenous technology<br>cumulation)<br>•<br>Operation &Maintenance (O&M) learning rate (global exogenous<br>technologycumulation)|
|LCOEs (mean and standard deviation)|•<br>Investment costs subject to global learning rate<br>•<br>O&M costs subject to domestic learning rate<br>•<br>Fuel costs grow with international fuel price projections.<br>•<br>Policy options: investment subsidies, fuel tax and carbon tax<br>•<br>Total LCOE is the sum of investment, O&M, fuel costs and policy<br>costs|
|Max and Min|•<br>Penalties equations when share breaches exogenous maximum or<br>minimum limit.<br>•<br>Total augmented cost is total cost LCOE plus penalties (this is the<br>cost thatget used in thepreference equation)|
|Production|•<br>Production bytechnology|
|**Aggregated equations**||
|Electricity price|•<br>Weighted average LCOEs (without penalties)<br>•<br>Weighted average LCOEs(withpenalties)|
|Emissions|•<br>Aggregate CO2emissions|
|Investment|•<br>Total capital stock(delta = new investment less depreciation)|
|Fiscal|•<br>Carbon tax revenues<br>•<br>Energy tax revenues<br>•<br>Total subsidyspending|

The full detail of the FTT-FLEX equations is given in Annex I.

FTT-FLEX model is implemented in two versions: one coded in EViews and the other in Python using ModelFlow, a Python-based business logic programming language (Hansen, 2023). Both versions are available to users upon request.

# **3.5 FTT-FLEX application in other sectors**

The FTT-FLEX model has been deliberately designed to be adaptable to a variety of sectors. This study focusses on its application in the power sector given its status as a major polluter and the well-defined nature of its technologies. The FTT-FLEX framework can also be applied in other sectors that have clearly defined competing technologies and comprehensive technology information (including lead time, lifetime, costs, energy type per production unit, and existing shares), for instance:

- Passenger or freight transport: diesel, petrol, hybrid, hydrogen, and electric vehicles, with total demand assessed in terms of passenger kilometers.

- Heating: gas boilers, oil boilers, electric heaters, solar options, and heat pumps, with total demand measured in heating units.

10
<!-- Start of picture text --> Outputs from FTT FLEX<br>Electricity price<br>Other FTT feedbacks:<br>* Power sector Further impacts on<br>FURS. investment price, government<br>¢ Fiscal revenues MFMod balance, potential<br>(carbon tax and fuel outputs,..<br>tax) and spending<br>(subsidies)<br>Inputs to FTT FLEX<br>Electricity demand<br>(int + final)<br><!-- End of picture text -->
assessment of decarbonization of the power sector, but rather to showcase the strengths and weakness of using FTT-FLEX for such an exercise.

# **5.1 Background on the power sector in Guinea-Bissau**

Guinea-Bissau is a small coastal country, situated in West Africa. It is one of the world’s poorest and most fragile countries. Only 35% of its population has access to electricity (World Bank, 2021) and the country contributed to less than 0.01% of global emissions (UNDP, 2022). Given its modest size, limited energy consumption, and lack of data, it is not surprising that most global energy-economic models, including the main FTT model, do not include Guinea-Bissau.

Historically, Guinea-Bissau has depended heavily on fuel oil for electricity generation. However, since 2020, solar power has emerged as a significant player in the market, experiencing rapid growth from a 1% share in total power generation in 2020 to 7% in 2021.<sup>3</sup> The remaining oil-based electricity generation is supplied by a floating power station docked near its capital city, Bissau, and operated by the Turkish company Karpowership.<sup>4</sup> The country plans to build its first hydropower plant in 2024, which is expected to contribute approximately 9% to the country’s power mix. Currently, a 20MW solar PV power project is in the permitting stage and, if approved, is expected to come online in 2025,<sup>5</sup> nearly doubling its current total electric power generation capacity. Guinea-Bissau boasts significant solar potential, with the International Renewable Energy Agency (IRENA) indicating that 100% of the country's land area is suitable for an annual PV output of 1.6-1.8 (MWh/kWp<sup>6</sup> ), compared to a global average of 20%.

Unfortunately, the planned hydro plant as well as other energy infrastructure projects to enable power imports from its neighboring countries are facing severe delays,<sup>7</sup> suggesting that reliance on Karpowership will persist in the short to medium term.

# **5.2 The Guinea-Bissau 2020 least cost generation expansion plan**

The World Bank’s 2020 Power Sector Policy Note, using the least cost approach, recommended GuineaBissau move away from heavy-fuel oil for power generation towards low-cost electricity imports from the neighboring country of Guinea.

> 3 - - <u>https://www.irena.org/ /media/Files/IRENA/Agency/Statistics/Statistical_Profiles/Africa/Guinea Bissau_Africa_RE_SP.pdf</u>

> 4 <u>https://www.karpowership.com/</u>

> 5 <u>https://www.power-technology.com/data-insights/power-plant-profile-bissau-solar-pv-park-1-guinea-bissau/</u> 6 kWp = kilowatt peak power output.

> 7 Guinea-Bissau Public Policy Notes 2023:Energy, World Bank, forthcoming.

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_Figure 4 Guinea-Bissau’s projected electricity demand (MW) and the optimal least-cost energy mix 2020-2035, Least Cost Generation Expansion Plan, 2020._ <!-- Start of picture text --> 100%<br>286 90%<br>273<br>260 80%<br>247<br>234 70%<br>221<br>208 60%<br>195<br>169 182 50%<br>157<br>6390 68103 74118 79130 85142 90 96 101 107 112 118 123 129 134 140 145 40%30%20%10%<br>0%<br>2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035<br>Scénario Bas Scénario Haut Solaire PV Thermique Fioul Hydro Imports ENS<br><!-- End of picture text --> _Source(s) Figure taken from Guinea-Bissau: Power Sector Policy Note 2020, World Bank. Note(s): Solaire PV = Solar PV, Thermique Fioul = heavy-fuel oil, ENS = suppressed demand._ The least-cost approach employed in that study calculates the lowest cost energy mix for a specified level of electricity demand, based on assumptions regarding future technology costs (LCOEs) and a set of constraints, such as decarbonization goals or storage options. However, as discussed above, such approaches overlook the path dependency of technology diffusion and the role that endogenous cost reduction may play.

# **5.3 FTT-FLEX baseline for Guinea-Bissau’s power sector**

Based on historical power sector share data in 2020 and short-term projections up to 2025 that reflect the latest power sector developments mentioned above, FTT-FLEX calculates the power generation mix from 2026 onward to meet the projected electricity demand (growing at around 5% pa) coming from the Guinea-Bissau MFMod macroeconomic model. Due to a lack of data for cost, cost distribution, lifetime, and lead time, the FTT-FLEX analysis reported below excludes electricity imports from neighboring countries as competing technologies. With more data, these could be included.

The projected power sector baseline assumes that the rest of the world's energy transition is limited, consistent with the IEA’s World Energy Outlook 2022 Stated Policies Scenario (STEPS). However, within the global power sector, solar is anticipated to expand even when there are limitations on energy transitions in other areas. In FTT-FLEX, this manifests as a significant reduction in solar costs over time due to exogenous global learning – with the result that the projected levelized cost of electricity (LCOE) for solar in Guinea-Bissau is estimated to decrease from $62 per MWh in 2030 to $37.5 per MWh in 2050 in the baseline without any additional policies (all prices presented in real 2020 price terms).

The resulting baseline projections using FTT-FLEX show a notable increase in the share of solar in Guinea-Bissau, rising from just over 30% in 2025 to 56% in 2050. While diminished, oil generation is expected to retain a significant share at 36% in 2050 in this baseline scenario.

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<!-- Start of picture text --> 500 100%<br>450 90%<br>400 80%<br>350 70%<br>300 60%<br>250 50%<br>200 40%<br>150 30%<br>aeso 20%10%10%<br>0%<br>° 2025 2030 2038 2040 2088 2080 SERSSSSSASFSSSRSSTSSTSLSssssseseeseseseeseseesesegessggsgssssSSEssssseseeseseseeseseesesegessggsgssssSSESSE<br>Electricity demand (GWh) — —==Power sector CO2 emissions (ktCO2)<br>@Solar SOil mhydro<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%10%10%<br>0% SERSSSSSASFSSSRSSTSSTSLSssssseseeseseseeseseesesegessggsgssssSSEssssseseeseseseeseseesesegessggsgssssSSESSE<br>@Solar SOil mhydro<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>2 2020 2030 2040 2050<br>-4<br>-6<br>8<br>-10<br>12<br>-14<br>-16<br>-18<br>———=Flectricitydemand —<—Power sector CO2<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>NORNANNANADMOMMnnoMmMomnnnteryrrrrrrTtHooooooooooooosoooeoesocoeseoesceseseeeseNNNNNNNNNNNNNNNNNNNNNNNNNAHDOANMTATHNHORADSCHA AMTNHKWORADSNS<br>mSolar mOil; mhydro<br><!-- End of picture text -->
<!-- Start of picture text --> 20 100%<br>10 90%<br>0 80%<br>19 2020 2030 2040 2050 70%<br>60%<br>-20 50%<br>-30 40%<br>-40 30%<br>-50 20%<br>10%<br>-60<br>0%<br>-70 N oooooooooooooeosoeoeeoeseeeoeeeeeeeonoranoAannanoranoAannaAanna n nnmsrnvonraanoedrantunvemamammamononnnnterrrmamammamononnnnterrr r EAOaDSrrrTtrHrrrTtrHrH<br>NNNNNNNNNNNNNNNNNNNNNNNNNNN<br>==——Electricitydemand ——=Power sector CO2 .<br>@Solar Oil mhydro<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>N oooooooooooooeosoeoeeoeseeeoeeeeeeeonoranoAannanoranoAannaAanna n nnmsrnvonraanoedrantunvemamammamononnnnterrrmamammamononnnnterrr r EAOaDSrrrTtrHrrrTtrHrH<br>NNNNNNNNNNNNNNNNNNNNNNNNNNN<br>.<br>@Solar Oil mhydro<br><!-- End of picture text -->
<!-- Start of picture text --> 20<br>°<br>2020 2030 2040 2050<br>-20<br>-40<br>-60<br>80<br>-100<br>Electricity demand Power sector CO2<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0% NORADOANMTNHORADOANANMTHOYORADYSNauanaanmrmaonmmrmmnamanmnwasse srrrrrrsry<br>NNNNNNNNNNNNNNNNNNNNNNNNoooooooooooosoooosoeoeoscoeecesesaeeecseNS<br>mSolar sOil hydro<br><!-- End of picture text -->
# **Box 2: Economic feedbacks from FTT-FLEX to MFMod**

The overall effects of power sector policies on GDP are influenced by three primary factors: the additional investment required, changes in the level of electricity supply, and the changes to electricity price.

The investment feedbacks from FTT-FLEX to MFMod are formally introduced as follows: • Changes in investment costs to produce the same amount of electricity ( c ) do not add to country’s capital stock formulation ( ) cctt,tt • Changes in investment required to produce additional electricity supply ( ∆II_EE S S S ) are tt KK added to country’s capital stock formulation ( ) ,tt ∆II_EE The MFMod model capital stock formulation is adjusted to include electricity investment feedback. KKtt c S S S Where is the depreciation rate and KKtt = (1 −δδ)KKtt−1 is total investment. + IItt − ∆II_EE cctt,tt + ∆II_EE ,tt And the model potential output equation is adjusted to take into account of the new capital stock. δδ IItt ∗ (1−∝) ∗(∝) Where is Total Factor Productivity, ∝YY is the labor share, tt = AAttKKtt−1 NNtt is the structural employment. Additionally, the changes in electricity price (average LCOE across the technologies in use) affects AAtt NN<sup>∗</sup> Additionally, the changes in electricity price (average LCOE across the technologies in use) affects electricity demand and the price level (inflation) in the economy. There is also feedback to fiscal balance and government spending (subsidies), taxation (carbon tax and energy tax), debt and interest rates that will be sensitive to the recycling assumptions made about any fiscal measures imposed, including carbon taxes.

A revenue neutral policy introduction of a carbon tax could see other taxes potentially more distorting reduced with positive impacts for GDP. Similarly, a fiscal balance neutral implementation where government transfers to households were introduced to compensate for higher carbon taxes would have different implications than one where the money was pushed into infrastructure investments.

# **5.7 Economic impacts of the power sector policy scenario in MFMod**

Table 3 summarizes the economic impacts of the different scenarios. In all scenarios, adjustments in average electricity prices as a result of policy trigger changes in electricity demand, and FTT-FLEX adjusts its electricity supply to meet the new demand level. This shift in supply necessitates a change in the country's capital stock. Essentially, higher (lower) prices make electricity less (more) affordable, resulting in a decrease (increase) in both consumption and production. Here, we assume that electricity supply firms pass on any changes in costs to end users, and no compensations are provided to firms affected by regulations (see Limitation in the next section).

Consequently, the carbon tax scenario results in negative GDP outcomes due to higher electricity prices. In contrast, the solar kick-start policies and a lifetime regulatory framework for oil power plants 17
generate positive economic impacts. The shift to solar reduces electricity prices, enhancing affordability and leading to an increase in both electricity demand and supply. An exception occurs in the short term after the regulation is introduced in 2025, causing an immediate rise in electricity prices and resulting in short-term negative GDP impacts. This increase is attributed to the higher capital cost associated with oil power generation due to the shorter project lifetime. The augmented capital cost also applies to existing oil power plants, and this expense is passed on to the average electricity price before the transition to alternative sources of electricity can take place.

_Table 3: Summary of impacts in the regulation scenario, % difference from BAU unless otherwise stated._

||**2025**|**2030**|**2040**|**2050**|
|---|---|---|---|---|
|**Carbon tax**|||||
|GDP Impacts|0.0|-0.1|-0.9|-2.8|
|Changes in electricitysupply|-5.5|-4.8|-6.7|-9.4|
|Electricity price|12.8|17.2|30.5|49.2|
|Debt share of GDP(absolute differences)|0.0|-0.1|-0.9|-1.3|
|**Solar kick-start policies**|||||
|GDP Impacts|0.0|0.0|0.5|2.8|
|Changes in electricitysupply|1.9|3.6|7.3|10.2|
|Electricity price|-4.0|-9.7|-22.2|-32.5|
|Debt share of GDP(absolute differences)|0.0|0.0|-0.3|-0.8|
|**Oil plant life time regulations**|||||
|GDP Impacts|0.0|-0.2|0.3|3.4|
|Changes in electricity supply|-3.3|2.7|11.8|13.3|
|Electricity price|7.5|-4.8|-30.4|-40.8|
|Debt share of GDP (absolute differences)|0.0|0.0|0.0|-0.8|

_Source(s): FTT-FLEX_ Average electricity prices go down in the solar kick-start policies and oil plant lifetime regulation (in the long run) scenarios because the policies increase the share of solar relative to the baseline. In the baseline, the average cost of solar is already lower than oil and coming down. Solar’s share remains relatively low despite that cost advantage because of the incumbent advantage of oil. As oil capacity shuts and is replaced by solar, that incumbency advantage shifts toward solar and electricity prices start to come down rapidly and the share of solar rise even further. The decrease in prices stimulates demand for electricity, which is increasingly met by the lower cost and now more abundant solar. The capital associated with the increase in supply of electricity is then added to the country's capital stock and increases potential output.

The carbon tax scenario results in additional revenues for the government, contributing to debt reduction. As the solar kick-start policies assume international funding, there are no direct impacts on the country's public balance and the effects on debt are by design minimal in the regulatory scenario. This assumption is based on the regulation working effectively, having negligible implementation costs, and not requiring any fines. Instead, if we relax these assumptions then there will be direct impacts on the fiscal balance.

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In certain instances, the government might opt to compensate stranded power generation assets<sup>8</sup> due to regulatory measures. In the context of Guinea-Bissau, as mentioned above, this could entail an early termination of the contract with Karpowership, and the associated cost would contribute to higher public debts.

# **6. Limitations**

In many countries, the characteristics of the local energy market might not align well with the application of FTT-FLEX, particularly when decision-making is not driven by private investors. For instance, if the power sector is predominantly governed by a limited number of public entities responsible for determining the energy mix and expansion plans, or if the sector operates under substantial regulatory control, allowing minimal influence of market-based policies on decisions.

Another example occurs when the existing domestic infrastructure or available resource potential significantly influences technology choices within a country. Currently, these factors are not accounted for in the LCOE comparisons. Therefore, users are required to input external constraints manually, such as maximum or minimum limits, or even the technology share, to accurately reflect these influences.

In such scenarios, the agent-based technology diffusion approach with endogenous learning, like FTT, may not effectively model the decisions made by a social planner.

Nevertheless, the FTT-FLEX framework remains a valuable tool in these cases, as planners can override technologies exogenously to reflect their planned power sector mix. This adjustment allows for the extraction of valuable insights, such as CO2 intensity, electricity pricing, and required investments for the planned power mix. These insights can then be utilized to calculate electricity demand and other economic implications within macroeconomic models.

# **7. Discussion**

This paper introduces a new model, FTT-FLEX, a low-data variant of the FTT framework (Mercure, 2012), which has been used for over a decade. FTT-FLEX can be used as a stand-alone model or in combination with a single-country macroeconomic model. FTT-FLEX captures most of the country-specific dynamics of FTT but streamlines data requirements by treating global effects as exogenous inputs.

The FTT-FLEX framework offers an alternative to least-cost models not only in the power sector, but also in other sectors characterized by competing technologies. It adopts a diffusion theory–based approach for modeling technology selection. While least-cost models, grounded in engineering perspectives with a normative systems optimization philosophy, are effective for planning purposes, the FTT modeling framework may be more relevant for simulating alternative policies from a given baseline, especially in instances where the speed of diffusion of new technologies is sensitive to countries’ experience with them. The integration of FTT-FLEX with macroeconomic models, such as MFMod, establishes a valuable tool for analyzing the impacts of climate mitigation policies on both emissions and the economy.

> 8 Past applications of the main FTT model suggest that the stranded costs in the power sector resulting from the early closure of fossil fuel power plants are relatively minor in the context of global decarbonization. The more substantial stranded costs arise from fossil fuel resources that will remain unused in fossil fuel exporting nations (Mercure et al, 2018b and Semieniuk et al, 2022).

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The version of the model presented here captures the main dynamics of technology diffusion at the country level for the power sector and its interactions with a macro model. Nevertheless, several extensions remain possible:

- Domestic learning rates on operation and maintenance costs, which are likely to benefit from learning by doing within the country. Currently, capital and O&M costs are assumed to be affected only by the global learning rate (exogenous in FTT-FLEX).

- Diminishing returns as natural resources or suitable sites are depleted. Currently, this is addressed through an exogenous assumption regarding the maximum domestic capacity limit for each technology. This treatment could be replaced with a more gradual representation of diminishing capacity as resources approach their maximum threshold.

- A feedback loop from the macro model to capture the impacts of changes in the interest rate and discount rate could be introduced.

- Introduce dispatch, storage and intermittency of renewables treatments in FTT-FLEX power.

These features will be considered in the next version of the FTT-FLEX and MFMod models.

Furthermore, detailed examinations of diverse applications of FTT-FLEX in various sector contexts, including transport, heating and cooling, and industry, are planned for further study and analysis.

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