Dont Mess with Texas: Getting the Lone Star State to Net-Zero by 2050
Bibliographic record
Abstract
The world is decarbonizing. Many countries, companies, and financial institutions have committed to cutting their emissions. Decarbonization commitments have been issued by: 136 countries including Canada, China, and the UK, at least 16 U.S. states including New York, Louisiana, and Virginia, and a third of the largest 2,000 publicly traded companies in the world, including Apple, Amazon, and Walmart, and numerous Texas companies like ExxonMobil, American and Southwest Airlines, Baker Hughes, and AT&T.1–9 These decarbonizing countries, states, cities, and companies are Texas's energy customers. If Texas ignores the challenge to decarbonize its economy, it may eventually face the more difficult challenge of selling carbon-intensive products to customers around the world who do not want them. We are already seeing this scenario beginning to play out with France canceling a liquified natural gas deal from Texas gas producers and both U.S. and international automakers announcing shifts to electric vehicles. Proactive net-zero emissions strategies might allow Texas to maintain energy leadership and grow the economy within a rapidly decarbonizing global marketplace.Thankfully, Texas is uniquely positioned to lead the world in the transition to a carbon-neutral energy economy. With the second highest Gross State Product in the US, the Texas economy is on par with countries like Canada, Italy, or Brazil. Thus, Texas's decisions have global implications. Texas also has an abundant resource of low-carbon energy sources to harness and a world-class workforce with technical capabilities to implement solutions at a large-scale quickly and safely. Texas has a promising opportunity to lead the world towards a better energy system in a way that provides significant economic benefits to the state by leveraging our renewable resources, energy industry expertise, and strong manufacturing and export markets for clean electricity, fuels, and products. The world is moving, with or without Texas, but it is likely to move faster--and Texas will be more prosperous--if Texans lead the way.There are many ways to fully decarbonize the Texas economy across all sectors by 2050. In this analysis, we present a Business as Usual (BAU) scenario and four possible pathways to Texas achieving state-wide net-zero emissions by 2050. Figure ES-1 provides a visual comparison of scenario conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.082 | 0.022 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".