📈 economics

Is Statutory Renewable Portfolio Standard Stringency Robustly Associated with Real Gross State Product? The case of New Mexico

This study finds no robust long-run or predictive relationship between the stringency of New Mexico's statutory Renewable Portfolio Standard and its real gross state product, suggesting that aggregate output alone is insufficient to measure the policy's broader contributions to sustainable development goals.

Original authors: Iqbal Pervez, Ivy Nasreen

Published 2026-09-28

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CC BY 4.0

Original authors: Iqbal Pervez, Ivy Nasreen

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

In the United States, many states have adopted rules called Renewable Portfolio Standards. Think of these as legal promises made by governments to their power companies. The promise is simple: by a certain date, a specific slice of the electricity sold to homes and businesses must come from clean sources like wind and sun, rather than from burning coal or gas. These rules are designed to fight climate change and build a cleaner energy future. But for decades, a quiet debate has simmered among economists and policymakers. While everyone agrees these rules help the environment, no one is entirely sure how they affect the overall health of a state's economy. Does forcing a switch to green energy create new jobs and boost local wealth, or does the cost of building new power lines and buying expensive technology slow down economic growth? The answer matters deeply, because if the rules hurt the economy, they might be hard to keep; if they help, they could be a model for the rest of the country.

Two researchers, Iqbal Pervez from New Mexico State University and independent scholar Ivy Nasreen, decided to look closely at this question using the state of New Mexico as their laboratory. New Mexico is an ideal place to study this because it has a long history of renewable energy laws, starting in the early 2000s, and it possesses vast wind and solar resources. The team gathered thirty years of data, from 1997 to 2024, tracking the state's total economic output, known as the Real Gross State Product, alongside the strictness of its renewable energy laws. They also carefully accounted for the fact that New Mexico's economy does not exist in a vacuum; it rises and falls with the national economy of the United States. By using advanced statistical tools designed to separate short-term bumps from long-term trends, they tried to see if the tightening of the renewable energy rules was directly linked to the state's economic performance.

The researchers built a detailed model to test whether the stringency of the renewable energy laws had a predictable effect on the state's wealth. They found that while the model could explain a great deal of the state's economic movement, the renewable energy rules themselves did not show a clear, strong connection to the overall economic growth. When they looked for a long-term relationship, the data was inconclusive. The statistical tests they used could not confirm that the renewable energy laws were driving the economy up or down. In fact, the numbers suggested a very slight negative link, but the evidence was too weak to say this was a real effect rather than just a random fluctuation in the data. The researchers also checked if the renewable rules could predict future economic changes, or if the economy could predict changes in the rules, and found no such pattern. The results held up even after they checked for errors in the data and tested the stability of their model over time.

This does not mean that renewable energy policies have no impact at all. The study simply found that the total economic output of the state, as measured by a single number, does not move in a predictable way based on how strict the renewable energy laws are. The researchers explain that this is likely because the economy is a complex machine. The benefits of new jobs in wind farms might be canceled out by higher electricity bills for factories, or the growth in one county might be hidden by a slowdown in another. The state's economy is also heavily influenced by national trends, oil and gas prices, and federal policies, which can drown out the specific signal of the renewable energy rules. The study concludes that looking at the total size of the economy is not a good way to judge whether these policies are working. Instead, to understand the true value of renewable energy laws, one must look at a wider dashboard of indicators, including how much clean energy is actually being generated, how much it costs families, how many quality jobs are created, and how much pollution is reduced. The rules may be doing important work for the climate and for local communities, but that work does not necessarily show up as a simple rise in the state's total economic numbers.

Technical Summary: Is Statutory Renewable Portfolio Standard Stringency Robustly Associated with Real Gross State Product? The case of New Mexico

Problem Statement and Research Gap
Renewable Portfolio Standards (RPS) are widely utilized state-level mechanisms to accelerate renewable electricity adoption. However, the empirical literature regarding their impact on aggregate economic performance remains inconsistent. Existing studies often conflate renewable investment with actual capacity or production, fail to account for the heterogeneity in policy design, and frequently overlook the temporal dynamics of diffusion and structural breaks. Furthermore, much of the literature focuses on cross-sectional averages or specific outcomes (e.g., emissions, prices) rather than examining the historical relationship between statutory policy stringency and aggregate real output within a single state.

This study addresses these gaps by investigating whether the stringency of New Mexico's statutory RPS is robustly associated with the state's Real Gross State Product (RGSP) from 1997 to 2024. The research acknowledges that while RPS policies contribute to Sustainable Development Goals (SDGs) 7, 8, 9, and 13, it is unclear if a single aggregate economic indicator (RGSP) can capture the policy's broader sustainability effects or if statewide output masks significant spatial redistribution and delayed adjustment processes.

Methodology
The study employs a longitudinal time-series approach using 28 annual observations (1997–2024), resulting in 27 usable observations for the preferred model after accounting for lags. To address the identification challenges posed by the national business cycle and the deterministic nature of the RPS policy (a step function rather than a stochastic process), the authors utilize a multi-method framework:

ARDL Bounds Testing: An Autoregressive Distributed Lag (ARDL) model is estimated to separate short-run dynamics from long-run level relationships. The preferred specification, ARDL(1,0,1,1,1,1), controls for employment, electricity prices, inflation, and crucially, U.S. Real GDP to isolate state-specific effects from national trends. The model selection is based on the Akaike Information Criterion (AIC).
Finite-Sample Bounds Test: To test for cointegration, the study applies the Pesaran-Shin-Smith bounds test using finite-sample critical values (Kripfganz–Schneider) to account for the small sample size.
Toda-Yamamoto Causality Test: A lag-augmented Vector Autoregression (VAR) is employed to test for Granger causality in both directions (RPS stringency $\leftrightarrow$ RGSP), ensuring robustness against potential non-stationarity without requiring pre-testing for cointegration.
Diagnostic Checks: The models undergo rigorous diagnostic testing for serial correlation (Breusch-Godfrey), heteroskedasticity (Breusch-Pagan), normality of residuals, functional form misspecification (Ramsey RESET), and parameter stability (CUSUM).

Key Results
The empirical analysis yields the following findings:

Model Fit and Dynamics: The preferred ARDL model demonstrates a high explanatory power ( $R^2 = 0.9904$ ). The error correction term (adjustment coefficient) is negative and statistically significant ($-0.4187$, $p=0.018$ ), indicating that approximately 42% of deviations from the dynamic path are corrected annually.
Long-Run Association: The estimated long-run coefficient for RPS stringency is negative ($-0.0155$) and only marginally significant ( $p=0.092$ ). This suggests a potential inverse relationship where a one-percentage-point increase in the statutory requirement is associated with a roughly 1.55% lower long-run RGSP, holding other factors constant.
Bounds Test Decision: Crucially, the finite-sample bounds test is inconclusive at the 10% level and fails to reject the null hypothesis of no level relationship at the 5% or 1% levels ( $F = 3.601$ ; $t = -2.647$ ). Consequently, the study cannot assert the existence of a stable long-run equilibrium relationship between RPS stringency and RGSP.
Causality: The Toda-Yamamoto modified Wald tests fail to reject the null hypothesis of no causality in either direction. When controlling for U.S. Real GDP, RPS stringency does not Granger-predict New Mexico's RGSP, nor does RGSP predict RPS stringency.
Diagnostics: The ARDL model passes all diagnostic tests for serial correlation, heteroskedasticity, normality, and stability. The VAR model is dynamically stable but rejects joint residual normality, leading the authors to treat VAR results as supplementary rather than definitive.

Key Contributions and Significance
The primary contribution of this paper is a rigorous, ex-post time-series evaluation of the link between statutory policy intensity and aggregate economic output in a single state, explicitly controlling for national economic cycles. The study challenges the assumption that RPS adoption automatically translates into measurable aggregate economic growth or decline at the state level.

The authors claim that their findings are significant for policy interpretation in three ways:

Limitations of Aggregate Metrics: The study demonstrates that RGSP alone is an insufficient metric for evaluating the success of renewable energy policies. The lack of a robust aggregate relationship does not imply the absence of economic effects; rather, it suggests that effects may be heterogeneous, sector-specific, spatially redistributed, or too small to be detected in a short annual time series.
Policy Evaluation Framework: The paper argues that policymakers should not rely on the assumption that higher statutory RPS percentages will automatically drive RGSP growth. Instead, evaluation should shift toward a "dashboard" approach monitoring specific indicators aligned with the SDGs: renewable deployment and affordability (SDG 7), employment and productivity (SDG 8), infrastructure and grid integration (SDG 9), and emissions (SDG 13).
Methodological Caution: The study highlights the statistical limitations of using small-sample, single-state time-series data for causal inference. It emphasizes the need to distinguish between statutory targets and actual compliance/deployment and warns against over-interpreting marginal coefficients without confirmed cointegration.

Conclusion
The paper concludes that there is no robust long-run or predictive relationship between the stringency of New Mexico's statutory RPS and its aggregate real gross state product over the 1997–2024 period. While the policy may influence specific sectors, local economies, or environmental outcomes, these effects do not manifest as a statistically significant, stable equilibrium shift in the state's total output when national business cycles are controlled for. The authors advocate for a more nuanced, multi-indicator approach to assessing the economic performance of renewable energy mandates, moving beyond aggregate output to capture the full spectrum of sustainability goals.

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