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Related Concept Videos

The Uncertainty Principle04:08

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines. While...
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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Uncertainty in Estimates, Incentives, and Emission Reductions in REDD+ Projects.

Jichuan Sheng1,2, Weihai Zhou3, Alex de Sherbinin4

  • 1Institute of Climate Change and Public Policy, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, Jiangsu, China. jsheng@nuist.edu.cn.

International Journal of Environmental Research and Public Health
|July 25, 2018
PubMed
Summary

Incentives can improve Reducing Emissions from Deforestation and Degradation (REDD+) project performance by reducing monitoring errors and transaction costs, ultimately benefiting stakeholders more effectively.

Keywords:
REDD+deforestationincentiveperformanceuncertainty

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Area of Science:

  • Environmental economics
  • Climate change mitigation policy
  • Forestry science

Background:

  • Accurate monitoring of emission reductions is crucial for the success of Reducing Emissions from Deforestation and Degradation (REDD+) initiatives.
  • Uncertainty in emission reduction estimates can negatively impact the performance and financial viability of REDD+ projects.
  • Transaction costs associated with monitoring and verification present a significant challenge for REDD+ implementation.

Purpose of the Study:

  • To investigate the impact of uncertainty and incentive mechanisms on the performance and stakeholder benefits within REDD+ projects.
  • To evaluate the potential of incentive policies to mitigate monitoring errors and reduce transaction costs in REDD+.
  • To compare equilibrium errors, emission reductions, and stakeholder benefits across various scenarios.

Main Methods:

  • Utilized Stackelberg economic models to analyze the strategic interactions between stakeholders in REDD+ projects.
  • Employed simulation research to quantify the effects of uncertainty and incentive levels on project outcomes.
  • Performed comparative analysis of different scenarios to assess the influence of errors on carbon emission measurements and compensation.

Main Results:

  • Emission reduction estimates are significantly affected by monitoring errors, influencing carbon emission values and compensation payments.
  • Incentive policies for investors were found to effectively reduce monitoring errors.
  • Improved monitoring accuracy through incentives leads to enhanced overall performance of REDD+ projects.

Conclusions:

  • Incentives are a valuable policy tool for enhancing the performance of REDD+ projects by addressing monitoring errors and transaction costs.
  • Providing incentives directly to investors, rather than landholders, is recommended for maximizing the positive impact on REDD+ project outcomes.
  • Addressing uncertainty through well-designed incentive structures is key to realizing the full potential of REDD+ for climate change mitigation.