KEY PERFORMANCE METRICS FOR CLEAN ENERGY APPLICATION CENTERS: FISCAL YEAR 2011

AbstractOn January 27, 2021, President Biden signed an executive order, Tackling the Climate Crisis at Home and Abroad, committing the United States to various goals within his campaign’s major climate policy, the Biden Plan for a Clean Energy Revolution and Environmental Justice. Included in this executive order is a commitment to “conserving at least 30 percent of [the United States’] lands and oceans by 2030.” This ambitious conservation target signals a promising direction for biodiversity in the United States. However, while the executive order outlines several goals for climate mitigation, the ‘30×30’ target remains vague in its objectives, actions, and implementation strategies for protecting biodiversity. Biodiversity urgently needs effective conservation action, but it remains unclear where and what this 30% target will be applied to. Achieving different climate and biodiversity objectives will require different strategies and, in combination with the associated costs of implementation, will lead to different priority areas for conservation actions. Here, we illustrate what the 30% target could look like across four objectives reflective of the ambitious goals outlined in the executive order. We compile several variations of terrestrial protected area networks guided by these different objectives and examine the trade-offs in costs, ecosystem representation, and climate mitigation potential between each. We find little congruence in priority areas across objectives, emphasizing just how crucial it will be for the Biden administration to develop clear objectives and establish appropriate performance metrics from the outset to maximize both conservation and climate outcomes in support of the 30×30 target. We discuss important considerations that must guide the administration’s conservation strategies in order to ensure meaningful conservation outcomes can be achieved over the next decade.

Download Full-text

Optimization of electrocatalyst performance of platinum–ruthenium induced with MXene by response surface methodology for clean energy application

Journal of Cleaner Production ◽

10.1016/j.jclepro.2020.123395 ◽

2020 ◽

Vol 277 ◽

pp. 123395 ◽

Cited By ~ 1

Author(s):

Norulsamani Abdullah ◽

R. Saidur ◽

Azran Mohd Zainoodin ◽

Navid Aslfattahi

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Clean Energy ◽

Energy Application

Download Full-text

Northeast Clean Energy Application Center

10.2172/1124076 ◽

2013 ◽

Author(s):

Tom Bourgeois

Keyword(s):

Clean Energy ◽

Energy Application

Download Full-text

Using QOPI metrics for physician incentive compensation.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.34_suppl.241 ◽

2012 ◽

Vol 30 (34_suppl) ◽

pp. 241-241

Author(s):

Grace Makari-Judson ◽

Tamara Wrenn ◽

Wilson C. Mertens ◽

Gordon Josephson ◽

James A. Stewart

Keyword(s):

Continuous Improvement ◽

Performance Metrics ◽

Financial Incentive ◽

Internal Audit ◽

Office Visit ◽

Well Being ◽

Fiscal Year ◽

Comprehensive Method ◽

Base Salary ◽

Quality Incentive

241 Background: Physician incentive or variable compensation (VC) models are typically based on work relative value units (wRVU). Baystate Medical Practices (BMP), a group of over 450 practitioners, is evaluating a division based pilot program for VC that, while it includes wRVUs, also includes other important performance metrics. QOPI is emerging nationally as a comprehensive method for assessing quality across oncology practices. We describe the use of QOPI measures as part of a financial incentive plan for a hospital-based oncology division. Methods: A nine member QOPI certified Oncology Division participated in a pilot VC plan with group specific targets selected based on prior below average performance. 20 overall weighting percent (10% weight per category) of the overall VC framework was linked to success in two QOPI categories; “completion of treatment summaries (RxS) within 90 days of end of chemo” and “assessment of patients’ emotional well being by second office visit.” Three tiers of achievement were set for each goal. For RxS completion, Tiers I, II and III were defined as completion of 15, 25, and 40% of charts respectively as measured by an internal audit. For assessment of patient emotional well being tiers were at the 75th, 80th, and 85th percentiles of national QOPI participants. A formula combining VC goals into year-end VC payout was driven by three levels of percent base salary: 8, 12, and 24% respectively. The probability of achievement of each goal was expected to be 90, 50, and 10% respectively. Educational discussions with BMP leadership were held to teach about QOPI validity in the oncology community. Results: BMP fiscal year ends September 30, thus final numbers are pending. With systems based improvements, Tier I achievement for emotional well being and Tier II achievement for RxS completion is expected. The division is interested in using different QOPI measures for VC next year with the goal of continuous improvement in QOPI scores. Conclusions: QOPI metrics can be used as a quality incentive for oncologists in the setting of a VC plan. Non-oncologists can appreciate the strength of QOPI as a quality tool. With time, the combination of a QOPI program through ASCO and use of various QOPI metrics can drive continuous improvement in an oncology group.

Download Full-text