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Record W7010231954

How the Current Siting Regime Stifles Renewable Energy

2022· article· en· W7010231954 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energySoftware deploymentObstacleReferendumGovernment (linguistics)ElectricityState (computer science)Energy policy
DOInot available

Abstract

fetched live from OpenAlex

The results of a little-discussed referendum in Maine in early November 2021 offered a dire warning for the future of clean energy in the United States. Voters in Maine overwhelmingly rejected the construction of a transmission line that would bring clean, renewable energy from Canada to New England. As the United States enters a “critical decade” in the fight against climate change, the need for rapid renewable energy deployment requires reassessing existing laws governing electricity generation and transmission projects, argues Uma Outka in a recent article. Siting laws and regulations govern where energy infrastructure can be located. Outka, a professor at the University of Kansas School of Law, contends that siting failures, such as the Maine transmission line, show the need for reforms that “anticipate the challenges” in siting renewable energy projects. Outka explains that although siting processes have undergone improvements, such as greater collaboration between developers, conservationists, and government agencies, the processes often remain “unpredictable.” She pinpoints the “varied patchwork” of state and local siting laws and regulations as one major obstacle facing the deployment of renewable energy. She also predicts that “not in my backyard” or “NIMBY” sentiments—on display recently in Maine—will continue to be another major obstacle to renewable energy infrastructure. To address these two interrelated problems—a patchwork of laws combined with NIMBYism—Outka identifies three “key areas of priority” for reforming siting to encourage renewable energy development. First, she recognizes the need to reexamine state and local government responsibilities in siting on privately owned land. As the primary regulators of land use, state and local governments wield a significant amount of power over siting decisions. Given the multiple actors and overlapping jurisdictions, however, the existing siting framework also creates a “highly variable” and fragmented regulatory environment. This variability often hampers new renewable developments by encouraging NIMBY advocates to veto renewable energy development and by creating regulatory complexity for developers. Unfortunately, as Outka asserts, not much has changed over the past decade. Despite the challenges posed by disjointed siting authority, New York undertook a successful reform effort in 2020. The state passed a new law that made several significant reforms to the siting regime. For example, the law strengthened the state’s ability to override municipal laws and regulations when those laws are found to be “unreasonably burdensome.” Furthermore, New York added new siting permit elements that require the identification of “host community benefits,” such as discounts on utility bills that result from new projects, Outka notes. She sees New York’s reforms as a model for how states can address fragmented siting authority and NIMBY sentiments. Outka concedes that political constraints may keep some states from reforming their siting laws as New York has done, but that all states can at least provide skeptical local communities with information about the benefits of renewable energy projects. Second, Outka recognizes the need to speed up siting processes for renewables on federally owned public lands. Although “approvals have been slow,” according to Outka, a clear foundation for faster federal siting processes exists. For example, the Bureau of Land Management’s solar and wind energy rule under the Obama Administration encourages renewable projects through faster approval processes and development incentives. Despite the underenforcement of the solar and wind energy rule during the Trump Administration, Outka urges the Biden Administration to “reinvigorate” this rule and other renewable policies that went ignored during the Trump Administration. In the offshore wind context, for example, where energy transmission infrastructure must be built in both federal and state waters, Outka advocates stronger cooperation between state and federal agencies. As one of the primary ways to improve offshore wind siting, Outka pushes for states and the federal government to follow conservationists’ guidance on “sensitive habitats” and reducing harmful encounters with wildlife. Finally, Outka discusses how renewable energy innovations, such as projects that combine energy generation and storage, present new kinds of siting problems that need to be addressed. She encourages altering siting regulations to avoid delays associated with novel projects. For example, Outka explains how a state regulatory board in Massachusetts delayed approval for a storage project due to a dispute over whether the facility should be categorized as “generation” or “transmission.” Outka also argues that storage projects, such as pumped storage hydropower, create additional siting reform needs. In the case of pumped storage, which generates energy from running water between reservoirs at different elevations, the Federal Energy Regulatory Commission maintains national control over siting approvals. As a result, Outka suggests the Commission create “consistent approaches to resolving environmental issues” when siting pumped storage projects. Outka concludes by reemphasizing the crucial role that siting plays in renewable energy development, especially for the coming decade. She asserts that siting reforms will catalyze the renewable energy transition in the battle against climate change.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.016
Scholarly communication0.0100.008
Open science0.0020.005
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0130.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.270
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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