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arxiv: 1907.11224 · v1 · pith:JWQETPKQnew · submitted 2019-07-11 · 💱 q-fin.GN

Does the short-term boost of renewable energies guarantee their stable long-term growth? Assessment of the dynamics of feed-in tariff policy

Milad Mousavian H. (1) , Hamed Shakouri G. (1) , Alinaghi Mashayekhi (2) , Aliyeh Kazemi (3) ((1) School of Industrial , Systems Engineering , University of Tehran , Iran (2) Graduate School of Management , Economics
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Sharif University of Technology Iran (3) Department of Industrial Management Iran)
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Pith reviewed 2026-05-24 22:45 UTC · model grok-4.3

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keywords renewable energyfeed-in tariffsystem dynamicsIranpolicy scenariosbudget feedbackelectricity taxfinancial crisis
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The pith

Adjusting the electricity consumption tax based on budget status produces the most stable long-term renewable capacity growth without financial crises or social backlash.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper models Iran's feed-in tariff policy for renewables using system dynamics to show that an early capacity increase can reverse if budget shortfalls erode social tolerance for the required tax and investor confidence. Three alternative policies are tested against the goal of reaching and sustaining 5 GW of renewable capacity. Continuation of the current approach with higher rates and adjusting rates themselves to the budget both produce later shortfalls or plant failures. Tying the tax rate on electricity consumption to the budget balance instead maintains steady growth, avoids deficits, and keeps social acceptance and investor trust intact.

Core claim

In the system dynamics model of Iran's 2015 FiT program, adjusting the tax on electricity consumption for renewable development according to budget status achieves the target installed capacity while preventing financial crises, negative social effects, and loss of investor trust, whereas the other two tested policies do not.

What carries the argument

A system dynamics model that links budget status to social tolerance for the renewable energy tax and to potential investors' trust, creating feedback loops that determine long-term capacity outcomes.

If this is right

  • Higher fixed FiT rates after 2021 trigger budget shortfalls that reduce new installations and can force existing plants offline.
  • Adjusting FiT rates themselves to the budget still leaves residual social or trust effects that limit long-term growth.
  • Budget-based tax adjustment meets the capacity target while keeping both social tolerance and investor trust above critical thresholds.
  • The model shows that early capacity gains are not self-sustaining if the funding mechanism creates accumulating budget pressure.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same budget-linked tax adjustment logic could be tested in other countries that rely on FiT or similar subsidy schemes once their renewable share grows large enough to affect public budgets.
  • If the modeled feedbacks prove accurate, governments could monitor a single budget indicator rather than multiple separate rates to keep renewable policy stable.
  • The approach implies that the political cost of the policy can be managed by making the tax adjustment rule transparent and automatic rather than discretionary.

Load-bearing premise

The feedback from budget status to social tolerance and investor trust dominates long-term renewable capacity results in Iran.

What would settle it

Whether raising the electricity consumption tax when the budget is tight and lowering it when the budget is strong produces sustained capacity additions past 2021 with no observed drop in social acceptance or investor withdrawals.

Figures

Figures reproduced from arXiv: 1907.11224 by Alinaghi Mashayekhi (2), Aliyeh Kazemi (3) ((1) School of Industrial, Economics, Hamed Shakouri G. (1), Iran), Iran (2) Graduate School of Management, Iran (3) Department of Industrial Management, Milad Mousavian H. (1), Sharif University of Technology, Systems Engineering, University of Tehran.

Figure 1
Figure 1. Figure 1: Subsystem diagram of the model. 5.2 Causal loop diagram The causal loop diagram for the study is shown in [PITH_FULL_IMAGE:figures/full_fig_p011_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Causal loop representation of FiT assessment model. The REs capacity growth influences the experience for using and making renewable systems. This learning results in lowering the capital cost, meaning the higher return of investment (ROI) and more tendency to invest in renewable resources. It also leads to more FiT requests, higher investments and then more installed capacity. This is [PITH_FULL_IMAGE:fi… view at source ↗
Figure 3
Figure 3. Figure 3: Stock-flow diagram of FiT effects on REs development [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Stock-flow diagram of installed capacity [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Annual FiT requests' causal relations. ROI of renewable projects O&M costs Capital cost FiT price Learning effect Intial capital cost Remuneration period Initial FiT price Intrest rate Effect of closeness to the goal on FiT price Learning curve parameter Cumulative installed capacity [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: ROI of renewable projects' causal relations [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Stock-flow diagram of FiT payment subsystem. 5.3.3 Budget [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Stock-flow diagram of the budget subsystem. 5.3.4 Social mechanisms Some social effects are considered in the model that are rarely mentioned in the previous researches. These are the effect of delay in debt payment on the tendency to invest, and on O&M activities that the owner of the power plants do, and the effect of REs tax on social acceptance. Mathematically, all of these effects are formulated by an… view at source ↗
Figure 10
Figure 10. Figure 10: Structurally oriented behavior test's behavior for SUNA debt and installed capacity. 5.4.2 Behavioral validity The historical data are too narrow due to the fact that FiT policy has been implemented in Iran since 2015. Therefore, it is hard to find a reliable reference mode, and this model should be seen as a laboratory to do what-if analysis rather than a tool for accurate numeric forecasting. However, t… view at source ↗
Figure 11
Figure 11. Figure 11: Simulated and historical installed capacity. 0 50 100 150 200 250 300 2015 2016 2017 2018 2019 Approved FIT request (MW) Time (Years) Simulated Historical [PITH_FULL_IMAGE:figures/full_fig_p028_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Simulated and historical approved FiT requests. The error analysis regarding the coefficient of determination (R2 ), the mean squared error (MSE), the root mean squared percent error (RMSPE), and the Theil inequality statistics for these two variables is presented in [PITH_FULL_IMAGE:figures/full_fig_p028_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Simulation results for SUNA debt versus budget [PITH_FULL_IMAGE:figures/full_fig_p030_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Simulation results for installed capacity [PITH_FULL_IMAGE:figures/full_fig_p030_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Simulation results for ROI of renewable projects. Tendency to invest 4 3 2 1 0 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 Time (year) Dmnl Tendency to invest : Base run [PITH_FULL_IMAGE:figures/full_fig_p031_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Simulation results for the tendency to invest. [PITH_FULL_IMAGE:figures/full_fig_p031_16.png] view at source ↗
Figure 18
Figure 18. Figure 18: Policy simulation results for installed capacity. [PITH_FULL_IMAGE:figures/full_fig_p034_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Policy simulation results for the tendency to invest [PITH_FULL_IMAGE:figures/full_fig_p035_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Policy simulation results for social acceptance. [PITH_FULL_IMAGE:figures/full_fig_p035_20.png] view at source ↗
read the original abstract

Feed in tariff (FiT) is one of the most efficient ways that many governments throughout the world use to stimulate investment in renewable energies (REs) technology. For governments, financial management of the policy is very challenging as that it needs a considerable amount of budget to support RE producers during the long remuneration period. In this paper, we illuminate that the early growth of REs capacity could be a temporary boost and the system elements would backlash the policy if financial circumstances are not handled well. To show this, we chose Iran as the case, which is in the infancy period of FiT implementation. Iran started the implementation of FiT policy in 2015 aiming to achieve 5 GW of renewable capacity until 2021. Analyses show that the probable financial crisis will not only lead to inefficient REs development after the target time (2021), but may also cause the existing plants to fail. Social tolerance for paying REs tax and potential investors trust emanated from budget related mechanisms are taken into consideration in the system dynamics model developed in this research to reflect those financial effects, which have rarely been considered in the previous researches. To prevent the financial crisis of the FiT funding and to maintain the stable growth in long term, three policy scenarios are analyzed: continuation of the current program with higher FiT rates, adjusting the FiT rates based on the budget status, and adjusting the tax on electricity consumption for the development of REs based on the budget status. The results demonstrate that adjusting the tax on electricity consumption for the development of REs based on budget status leads to the best policy result for a desired installed capacity development without any negative social effects and financial crises.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The paper develops a system dynamics model of Iran's feed-in tariff (FiT) policy for renewable energies, incorporating feedback loops from budget status to social tolerance for an RE tax and to investor trust. It simulates three policy scenarios (continuation with higher FiT rates, FiT adjustment based on budget, and tax adjustment based on budget) and concludes that adjusting the electricity-consumption tax based on budget status produces the best long-term capacity trajectory without financial crises or negative social effects.

Significance. If the model were fully documented and validated, the work would usefully illustrate how unmodeled social and financial feedbacks can reverse early FiT gains; the explicit comparison of three budget-linked policies is a concrete contribution. As presented, however, the simulation outputs rest on unshown structure and uncalibrated parameters, so the policy ranking cannot be assessed or reproduced.

major comments (3)
  1. [Model description] Model description (throughout): the two load-bearing feedback loops—budget status to social tolerance for RE tax to capacity, and budget status to investor trust to capacity—are asserted but never supplied with equations, table functions, delay times, or the stock-flow diagram; without these the simulation results that rank the tax-adjustment scenario as superior cannot be evaluated or replicated.
  2. [Results and policy scenarios] Results and policy scenarios: the headline claim that tax adjustment 'leads to the best policy result … without any negative social effects and financial crises' is generated solely by forward simulation; no sensitivity analysis on the free parameters (social tolerance, investor trust response), no calibration to Iranian budget or capacity time series, and no out-of-sample check against post-2015 data are reported, so any misspecification in the feedback gains would reverse the scenario ranking.
  3. [Abstract and model validation] Abstract and § on model validation: the central claim rests on the assumption that the two budget-related feedbacks dominate long-term outcomes, yet the manuscript provides neither parameter values, data sources, nor any test that these loops are identifiable from observed Iranian electricity-consumption or RE-capacity series.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which highlight important areas for improving the transparency and robustness of our system dynamics analysis. We address each major comment below and commit to revisions that enhance replicability without altering the core findings.

read point-by-point responses

  1. Referee: [Model description] Model description (throughout): the two load-bearing feedback loops—budget status to social tolerance for RE tax to capacity, and budget status to investor trust to capacity—are asserted but never supplied with equations, table functions, delay times, or the stock-flow diagram; without these the simulation results that rank the tax-adjustment scenario as superior cannot be evaluated or replicated.

    Authors: We agree that the manuscript did not provide sufficient detail on the model structure. In the revised version we will include a new appendix containing the complete set of equations, table functions for the two feedback loops, delay times, parameter values, and the stock-flow diagram. This addition will allow readers to fully evaluate and replicate the simulations. revision: yes

  2. Referee: [Results and policy scenarios] Results and policy scenarios: the headline claim that tax adjustment 'leads to the best policy result … without any negative social effects and financial crises' is generated solely by forward simulation; no sensitivity analysis on the free parameters (social tolerance, investor trust response), no calibration to Iranian budget or capacity time series, and no out-of-sample check against post-2015 data are reported, so any misspecification in the feedback gains would reverse the scenario ranking.

    Authors: We acknowledge that formal sensitivity analysis and calibration were not reported. We will add a dedicated sensitivity analysis section that systematically varies the social tolerance and investor trust parameters over plausible ranges and reports the resulting changes in scenario rankings. Parameter values were informed by Iranian energy-sector literature and expert input; we will document these sources explicitly and perform calibration against available budget and capacity series where data permit. Out-of-sample checks are constrained by the short post-2015 time series, but we will discuss this limitation and any available validation steps. revision: partial

  3. Referee: [Abstract and model validation] Abstract and § on model validation: the central claim rests on the assumption that the two budget-related feedbacks dominate long-term outcomes, yet the manuscript provides neither parameter values, data sources, nor any test that these loops are identifiable from observed Iranian electricity-consumption or RE-capacity series.

    Authors: We will revise both the abstract and the model validation section to list all parameter values, their data sources, and the rationale for focusing on the budget-related loops. We will also add explicit discussion of how these loops were identified from theoretical considerations and observed patterns in Iranian electricity data, while acknowledging the limits of identifiability testing given data availability. revision: yes

Circularity Check

0 steps flagged

No circularity: policy ranking emerges from forward simulation of an explicitly constructed model

full rationale

The paper builds a system-dynamics stock-flow model that incorporates budget-status feedbacks to social tolerance and investor trust, then runs three policy scenarios forward from 2015–2021 targets and beyond. The ranking of scenarios is an output of those simulations under the stated structure and parameter values; it is not obtained by fitting a parameter to the target trajectories and then re-labeling the fit as a prediction, nor by any self-citation that supplies the uniqueness of the model structure. No equation in the supplied text equates a derived quantity to its own input by construction, and the central claim therefore remains an independent consequence of the modeling choices rather than a definitional restatement of them.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The model depends on calibrated behavioral parameters for social tolerance and investor trust plus the structural assumption that system-dynamics feedback loops dominate real-world outcomes; no independent evidence for these parameters is supplied.

free parameters (2)
  • social tolerance for REs tax
    Behavioral parameter that determines how tax increases affect public support and therefore policy continuation; value chosen to reflect Iranian context.
  • investor trust response to budget status
    Parameter linking funding shortfalls to reduced new plant construction; calibrated within the model.
axioms (1)
  • domain assumption System dynamics can faithfully represent the coupled financial, social, and investment dynamics of a national FiT program.
    Invoked when the authors state that the model 'reflect[s] those financial effects' through social tolerance and investor trust.

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