Study Unlocks Opportunities To Bridge The US Wind Energy Workforce Gap

  • 📰 cleantechnica
  • ⏱ Reading Time:
  • 57 sec. here
  • 2 min. at publisher
  • 📊 Quality Score:
  • News: 26%
  • Publisher: 51%

Россия Последние новости Новости

Россия Последние новости,Россия Последние новости

Clean Tech News & Views: EVs, Solar Energy, Batteries

NREL Study, the First To Use a System Dynamics Model To Quantify This Gap, Outlines Possible Solutionsover the next few decades. With this growth, the demand for properly trained wind energy workers will also increase to meet national deployment targets.

, estimates that in 2030, the demand for workers could reach 258,000, whereas the supply of full-time workers might reach only 134,000—a shortfall of approximately 124,000 workers. “Although wind continues to be a major renewable energy source in the United States, there are still barriers to establishing a properly trained workforce that can meet future capacity goals,” said Jeremy Stefek, an NREL researcher and one of the report’s authors. “As we strive to achieve a 100% net-zero-carbon economy by 2050, it’s more important than ever to connect the dots between education, training, entry-level jobs, and long-term careers in the clean energy sector.

As with findings from NREL’s previous research, the survey described a wind workforce gap that features a lack of applicants with experience or required training and education, an inadequate number of job applicants, and job openings in geographic locations that are not where job seekers want to live.Despite its projected shortage of wind energy workers in the coming decades, the NREL report identifies strategies to close the wind energy workforce gap.

 

Спасибо за ваш комментарий. Ваш комментарий будет опубликован после проверки
Мы обобщили эту новость, чтобы вы могли ее быстро прочитать.Если новость вам интересна, вы можете прочитать полный текст здесь Прочитайте больше:

 /  🏆 565. in RU

Россия Последние новости, Россия Последние новости

Similar News:Вы также можете прочитать подобные новости, которые мы собрали из других источников новостей

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: OpportunitiesThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
Источник: hackernoon - 🏆 532. / 51 Прочитайте больше »