scholarly journals Carbon spot prices in equilibrium frameworks associated with climate change

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Zhenzhen Wang ◽  
Hao Dong ◽  
Zhehao Huang
Keyword(s):  
Climate Change ◽  
Optimization Problems ◽  
Dynamic Programming Principle ◽  
Carbon Trading ◽  
Spot Prices ◽  
Return Rates ◽  
Construct Optimization ◽  
The Fourier Transform ◽  
Carbon Prices ◽  
Theoretical Results

<p style='text-indent:20px;'>At present, it is believed that the best approach to mitigate global warming is the market-based formulation of carbon emission pricing. Thus, in this paper, we work on determining the carbon spot prices in a stochastic equilibrium framework associated with climate change. Two circumstances, differentiated by whether taking carbon trading in the market, are considered. We construct optimization problems and solve them by using dynamic programming principle. The Fourier transform and its properties are fully made use of to return the explicit formulas of carbon prices. In addition, some surprising but interesting properties of the carbon prices are also found. First, the carbon prices happen jumps at the end of the abatement period. Second, the return rates of carbon prices are completely dependent on the climate elements. Finally, we present some numeric results in response to our theoretical results.</p>

scholarly journals Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change

2015 ◽  
Vol 19 (2) ◽  
pp. 857-876 ◽  
Author(s):  
S. Wi ◽  
Y. C. E. Yang ◽  
S. Steinschneider ◽  
A. Khalil ◽  
C. M. Brown
Keyword(s):  
Climate Change ◽  
High Performance ◽  
Climate Models ◽  
Optimization Problems ◽  
Model Simulation ◽  
Model Fitting ◽  
Hydrologic Model ◽  
Simulation Accuracy ◽  
Distributed Models ◽  
Spatially Distributed

Abstract. This study tests the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve model simulation accuracy and understand prediction uncertainty at interior ungaged sites of a sparsely gaged watershed. The study is conducted using a distributed version of the HYMOD hydrologic model (HYMOD_DS) applied to the Kabul River basin. Several calibration experiments are conducted to understand the benefits and costs associated with different calibration choices, including (1) whether multisite gaged data should be used simultaneously or in a stepwise manner during model fitting, (2) the effects of increasing parameter complexity, and (3) the potential to estimate interior watershed flows using only gaged data at the basin outlet. The implications of the different calibration strategies are considered in the context of hydrologic projections under climate change. To address the research questions, high-performance computing is utilized to manage the computational burden that results from high-dimensional optimization problems. Several interesting results emerge from the study. The simultaneous use of multisite data is shown to improve the calibration over a stepwise approach, and both multisite approaches far exceed a calibration based on only the basin outlet. The basin outlet calibration can lead to projections of mid-21st century streamflow that deviate substantially from projections under multisite calibration strategies, supporting the use of caution when using distributed models in data-scarce regions for climate change impact assessments. Surprisingly, increased parameter complexity does not substantially increase the uncertainty in streamflow projections, even though parameter equifinality does emerge. The results suggest that increased (excessive) parameter complexity does not always lead to increased predictive uncertainty if structural uncertainties are present. The largest uncertainty in future streamflow results from variations in projected climate between climate models, which substantially outweighs the calibration uncertainty.

scholarly journals New Safe Approximation of Ambiguous Probabilistic Constraints for Financial Optimization Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
NingNing Du ◽  
Yan-Kui Liu ◽  
Ying Liu
Keyword(s):  
Value At Risk ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Random Perturbation ◽  
Probabilistic Constraints ◽  
Optimization Approach ◽  
Chinese Stock Market ◽  
Robust Solution ◽  
Return Rates ◽  
Financial Optimization

In financial optimization problem, the optimal portfolios usually depend heavily on the distributions of uncertain return rates. When the distributional information about uncertain return rates is partially available, it is important for investors to find a robust solution for immunization against the distribution uncertainty. The main contribution of this paper is to develop an ambiguous value-at-risk (VaR) optimization framework for portfolio selection problems, where the distributions of uncertain return rates are partially available. For tractability consideration, we deal with new safe approximations of ambiguous probabilistic constraints under two types of random perturbation sets and obtain two equivalent tractable formulations of the ambiguous probabilistic constraints. Finally, to demonstrate the potential for solving portfolio optimization problems, we provide a practical example about the Chinese stock market. The advantage of the proposed robust optimization method is also illustrated by comparing it with the existing optimization approach via numerical experiments.

Data Processing with Combined Homotopy Methods for a Class of Nonconvex Optimization Problems

2014 ◽  
Vol 1046 ◽  
pp. 403-406 ◽  
Author(s):  
Yun Feng Gao ◽  
Ning Xu
Keyword(s):  
Data Processing ◽  
Nonconvex Optimization ◽  
Normal Cone ◽  
Optimization Problems ◽  
Feasible Region ◽  
Specific Class ◽  
Homotopy Methods ◽  
Cone Condition ◽  
Nonconvex Optimization Problems ◽  
Theoretical Results

On the existing theoretical results, this paper studies the realization of combined homotopy methods on optimization problems in a specific class of nonconvex constrained region. Contraposing to this nonconvex constrained region, we give the structure method of the quasi-normal, prove that the chosen mappings on constrained grads are positive independent and the feasible region on SLM satisfies the quasi-normal cone condition. And we construct combined homotopy equation under the quasi-normal cone condition with numerical value and examples, and get a preferable result by data processing.

scholarly journals Forest carbon management and carbon trading: A review of Canadian forest options for climate change mitigation

2011 ◽  
Vol 87 (05) ◽  
pp. 625-635 ◽  
Author(s):  
Denise Golden ◽  
M.A. Smith ◽  
Stephen Colombo
Keyword(s):  
Climate Change ◽  
Boreal Forests ◽  
Policy Implications ◽  
Conservation Strategies ◽  
Emissions Reduction ◽  
Carbon Trading ◽  
Management Objectives ◽  
Trade Offs ◽  
Mitigate Climate Change ◽  
Forest C

Forests have significant potential to mitigate climate change. Canada has 30% of the world's boreal forests. The ratification of the Kyoto Protocol commoditized carbon (C) on an international scale. To achieve Canada's emission reduction targets and mitigate climate change, the potential of forest C offset projects and forest C trading is being evaluated. Carbon trading and forest C management have economic and policy implications and potential trade-offs in other forest management objectives. We discuss how forest C management and trading can contribute to global efforts for atmospheric greenhouse gas emissions reduction through either utilization and/or conservation strategies.

Iran deal will not relaunch nuclear power globally

2015 ◽  
Keyword(s):  
Climate Change ◽  
Power Generation ◽  
Nuclear Power ◽  
Nuclear Energy ◽  
Nuclear Power Generation ◽  
Content Type ◽  
Safety Risks ◽  
Un Security Council ◽  
Mitigate Climate Change ◽  
Carbon Prices

Subject Impact of the Iran deal on civil nuclear energy. Significance The deal between Iran and the P5+1 powers (five permanent UN Security Council members plus Germany) will have little effect on the global deployment of nuclear power technology. The agreement could pave the way for new civil nuclear power generation in Iran. Elsewhere, civil nuclear power's prospects are restrained by high costs compared to alternatives, safety risks and political acceptance. Impacts Advanced nuclear power countries will encourage newcomers to meet their needs for nuclear fuel by relying on existing suppliers. Countries investing in new civil nuclear power are unlikely to also seek weapons capability. Low oil and carbon prices and the apparent reluctance of countries to mitigate climate change will constrain nuclear power investment.

An Information-Theoretic Analysis on the Interactions of Variables in Combinatorial Optimization Problems

2007 ◽  
Vol 15 (2) ◽  
pp. 169-198 ◽  
Author(s):  
Dong-Il Seo ◽  
Byung-Ro Moon
Keyword(s):  
Information Theory ◽  
Combinatorial Optimization ◽  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Experimental Results ◽  
Theoretic Analysis ◽  
Information Theoretic ◽  
Combinatorial Optimization Problems ◽  
Theoretical Results

In optimization problems, the contribution of a variable to fitness often depends on the states of other variables. This phenomenon is referred to as epistasis or linkage. In this paper, we show that a new theory of epistasis can be established on the basis of Shannon's information theory. From this, we derive a new epistasis measure called entropic epistasis and some theoretical results. We also provide experimental results verifying the measure and showing how it can be used for designing efficient evolutionary algorithms.

scholarly journals ON ASSESSING THE DISCLOSURE RISK OF CONTROLLED ADJUSTMENT METHODS FOR STATISTICAL TABULAR DATA

2012 ◽  
Vol 20 (06) ◽  
pp. 921-941 ◽  
Author(s):  
JORDI CASTRO
Keyword(s):  
Minimum Distance ◽  
Optimization Problems ◽  
Statistical Disclosure Control ◽  
Tabular Data ◽  
Perturbative Approach ◽  
Disclosure Control ◽  
High Data ◽  
Disclosure Risk ◽  
Statistical Disclosure ◽  
Theoretical Results

Minimum distance controlled tabular adjustment is a recent perturbative approach for statistical disclosure control in tabular data. Given a table to be protected, it looks for the closest safe table, using some particular distance. Controlled adjustment is known to provide high data utility. However, the disclosure risk has only been partially analyzed using theoretical results from optimization. This work extends these previous results, providing both a more detailed theoretical analysis, and an extensive empirical assessment of the disclosure risk of the method. A set of 25 instances from the literature and four different attacker scenarios are considered, with several random replications for each scenario, both for L1 and L2 distances. This amounts to the solution of more than 2000 optimization problems. The analysis of the results shows that the approach has low disclosure risk when the attacker has no good information on the bounds of the optimization problem. On the other hand, when the attacker has good estimates of the bounds, and the only uncertainty is in the objective function (which is a very strong assumption), the disclosure risk of controlled adjustment is high and it should be avoided.

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