What is China's efficient power generation portfolio under carbon neutrality target? An empirical analysis considering flexibility and system cost.

Variable renewable energy (VRE) is the most promising form of primary generation under a carbon neutrality target due to its environmental benefits, incentive policy, and technological progress. However, the increasing proportion of VRE generation, such as solar and wind power, has sharply increased integration cost and reduced power grid stability. This study uses portfolio theory to investigate China's optimal power generation portfolio by 2050 considering flexibility constraint and system cost, including technical and integration costs. The results demonstrate that non-fossil-fuel power generation technologies have cost and emission reduction advantages over fossil-fuel-based technologies. VRE generation technologies must be developed in synergy with other forms of power generation when considering flexibility requirement and integration cost. A complete phase-out of fossil-fuel power generation technologies in China appears unlikely in the study period. Gas-fired and coal-fired power generation are the pillar forms of power generation to meet future flexibility needs.

A Quantum Double-or-Nothing Game: An Application of the Kelly Criterion to Spins.

A quantum game is constructed from a sequence of independent and identically polarised spin-1/2 particles. Information about their possible polarisation is provided to a bettor, who can wager in successive double-or-nothing games on measurement outcomes. The choice at each stage is how much to bet and in which direction to measure the individual particles. The portfolio's growth rate rises as the measurements are progressively adjusted in response to the accumulated information. Wealth is amassed through astute betting. The optimal classical strategy is called the Kelly criterion and plays a fundamental role in portfolio theory and consequently quantitative finance. The optimal quantum strategy is determined numerically and shown to differ from the classical strategy. This paper contributes to the development of quantum finance, as aspects of portfolio optimisation are extended to the quantum realm. Intriguing trade-offs between information gain and portfolio growth are described.

Coevolution of Patch Selection in Stochastic Environments.

AbstractSpecies interact in landscapes where environmental conditions vary in time and space. This variability impacts how species select habitat patches. Under equilibrium conditions, evolution of this patch selection can result in ideal free distributions where per capita growth rates are zero in occupied patches and negative in unoccupied patches. These ideal free distributions, however, do not explain why species occupy sink patches, why competitors have overlapping spatial ranges, or why predators avoid highly productive patches. To understand these patterns, we solve for coevolutionarily stable strategies (coESSs) of patch selection for multispecies stochastic Lotka-Volterra models accounting for spatial and temporal heterogeneity. In occupied patches at the coESS, we show that the differences between the local contributions to the mean and the variance of the long-term population growth rate are equalized. Applying this characterization to models of antagonistic interactions reveals that environmental stochasticity can partially exorcize the ghost of competition past, select for new forms of enemy-free and victimless space, and generate hydra effects over evolutionary timescales. Viewing our results through the economic lens of modern portfolio theory highlights why the coESS for patch selection is often a bet-hedging strategy coupling stochastic sink populations. Our results highlight how environmental stochasticity can reverse or amplify evolutionary outcomes as a result of species interactions or spatial heterogeneity.

Using modern portfolio theory to enhance ecosystem service delivery: A case study from China.

：Land management strategies often prioritize agricultural supply services at the expense of other ecosystem services. To achieve a high and steady supply of multiple ecosystem services, it is essential to optimize land management practices in areas suitable for agriculture. However, many studies on land management tend to focus on their benefits to ecosystem service delivery without adequately considering the potential risks to other services that might be involved. Here we use modern portfolio theory to quantitatively measure benefits and risks from land management strategies to enhance ecosystem services. We create seven land management scenarios that balance different kinds of ecosystem services in different ways in the agricultural production area of Maoming, Guangdong Province, China. The method yielded optimal portfolios of land management patterns that enhanced ecosystem services while reducing risk as much as possible. This includes a scenario delivering a 22% increase in agricultural production service, while simultaneously increasing the provision of nature-related ecosystem services by 2%. However, no optimization scenario was perfect, and there was always a trade-off between gaining certain ecosystem service benefits and creating a risk of losing others. Our portfolio theory approach reveals that it is essential to consider both the benefits and risks of land management strategies.

Model-free portfolio theory: A rough path approach.

Based on a rough path foundation, we develop a model-free approach to stochastic portfolio theory (SPT). Our approach allows to handle significantly more general portfolios compared to previous model-free approaches based on Föllmer integration. Without the assumption of any underlying probabilistic model, we prove a pathwise formula for the relative wealth process, which reduces in the special case of functionally generated portfolios to a pathwise version of the so-called master formula of classical SPT. We show that the appropriately scaled asymptotic growth rate of a far reaching generalization of Cover's universal portfolio based on controlled paths coincides with that of the best retrospectively chosen portfolio within this class. We provide several novel results concerning rough integration, and highlight the advantages of the rough path approach by showing that (nonfunctionally generated) log-optimal portfolios in an ergodic Itô diffusion setting have the same asymptotic growth rate as Cover's universal portfolio and the best retrospectively chosen one.

Pandemic portfolio choice.

COVID-19 has taught us that a pandemic can significantly increase biometric risk and at the same time trigger crashes of the stock market. Taking these potential co-movements of financial and non-financial risks into account, we study the portfolio problem of an agent who is aware that a future pandemic can affect her health and personal finances. The corresponding stochastic dynamic optimization problem is complex: It is characterized by a system of Hamilton-Jacobi-Bellman equations which are coupled with optimality conditions that are only given implicitly. We prove that the agent's value function and optimal policies are determined by the unique global solution to a system of non-linear ordinary differential equations. We show that the optimal portfolio strategy is significantly affected by the mere threat of a potential pandemic.

A Network Model Approach to International Aid.

Decisions made by international aid donors regarding the allocation of their aid budgets to recipients can be mathematically modelled using network theory. The many countries and multilateral organisations providing developmental aid, mostly to developing countries, have numerous competing or conflicting interests, biases and motivations, often obscured by a lack of transparency and confused messaging. Using network theory, combined with other mathematical methods, these inter-connecting and inter-dependent variables are identified, revealing the complicated properties and dynamics of the international aid system. Statistical techniques are applied to the vast amount of available, open data to first understand the complexities and then identify the key variables, focusing principally on bilateral aid flows. These results are used to create a weighted network model which is subsequently adapted for use by a hypothetical aid recipient. By incorporating modern portfolio theory into this weighted network model and taking advantage of a donor's reasons for allocating their aid budgets to that recipient, a simulation is carried out treating the problem as an optimal investment portfolio of aid determinant 'assets' which illustrates how a recipient can maximise their aid receipts. Suggestions are also made for further uses and adaptations of this weighted network model.

Multicriteria decisions and portfolio analysis: land acquisition for biological and social objectives.

Resource allocation for land acquisition is a common multiobjective problem that involves complex trade-offs. The National Wildlife Refuge System (NWRS) of the U.S. Fish and Wildlife Service currently uses the Targeted Resource Acquisition Comparison Tool (TRACT) to allocate funds from the Migratory Bird Conservation Fund (MBCF; established through the Migratory Bird Hunting and Conservation Act of 1934) for land acquisition based on cost-benefit analysis, regional priority rankings of candidate land parcels available for acquisition, and the overall biological contribution to duck population objectives. However, current policy encourages decision makers to consider societal and economic benefits of lands acquired, in addition to their biological benefits to waterfowl. These decisions about portfolio elements (i.e., individual land parcels) require an analysis of the difficult trade-offs among multiple objectives. In the last decade the application of multicriteria decision analysis (MCDA) methods has been instrumental in aiding decision makers with complex multiobjective decisions. In this study, we present an alternative approach to developing land-acquisition portfolios using MCDA and modern portfolio theory (MPT). We describe the development of a portfolio decision analysis tool using constrained optimization for land-acquisition decisions by the NWRS. We outline the decision framework, describe development of the prototype tool in Microsoft Excel, and test the results of the tool using land parcels submitted as candidates for MBCF funding in 2019. Our results indicate that the constrained optimization outperformed the traditional TRACT method and ad hoc portfolios developed using current NWRS criteria.

Diversification of forestry portfolios for climate change and market risk mitigation.

Investments in forestry are long-term and thus subject to numerous sources of risk. In addition to the volatility from markets, forestry investments are directly exposed to future impacts from climate change. We examined how diversification of forest management regimes can mitigate the expected risks associated with forestry activities in New Zealand based on an application of Modern Portfolio Theory. Uncertainties in the responses of Pinus radiata (D. Don) productivity to climate change, from 2050 to 2090, were simulated with 3-PG, a process-based forest growth model, based on future climate scenarios and Representative Concentration Pathways (RCPs). Future timber market scenarios were based on RCP-specific projections from the Global Timber Model and historical log grade prices. Outputs from 3-PG and the market scenarios were combined to compute annualized forestry returns for four P. radiata regimes for 2050-2090. This information was then used to construct optimal forestry portfolios that minimize investment risk for a given target return under different RCPs, forest productivity and market scenarios. While current P. radiata regimes in New Zealand are largely homogenous, our results suggest that regime diversification can mitigate future risks imposed by climate change and market uncertainty. Nevertheless, optimal portfolio compositions varied substantially across our range of scenarios and portfolio objectives. The application of this framework can help forest managers to better account for future risks in their management decisions.

Can Financial Economics Cure Cancer?

Funding for early-stage biomedical innovation has become more difficult to secure at the same time that medical breakthroughs seem to be occurring at ever increasing rates. One explanation for this counterintuitive trend is that increasing scientific knowledge can actually lead to greater economic risk for investors in the life sciences. While the Human Genome Project, high-throughput screening, genetic biomarkers, immunotherapies, and gene therapies have made a tremendously positive impact on biomedical research and, consequently, patient lives, they have also increased the cost and complexity of the drug development process, causing many investors to shift their assets to more attractive investment opportunities. This suggests that new business models and financing strategies can be used to reduce the risk and increase the attractiveness of biomedical innovation so as to bring new and better therapies to patients faster.

Adaptive Bet-Hedging Revisited: Considerations of Risk and Time Horizon.

Models of adaptive bet-hedging commonly adopt insights from Kelly's famous work on optimal gambling strategies and the financial value of information. In particular, such models seek evolutionary solutions that maximize long-term average growth rate of lineages, even in the face of highly stochastic growth trajectories. Here, we argue for extensive departures from the standard approach to better account for evolutionary contingencies. Crucially, we incorporate considerations of volatility minimization, motivated by interim extinction risk in finite populations, within a finite time horizon approach to growth maximization. We find that a game-theoretic competitive optimality approach best captures these additional constraints and derive the equilibria solutions under straightforward fitness payoff functions and extinction risks. We show that for both maximal growth and minimal time relative payoffs, the log-optimal strategy is a unique pure strategy symmetric equilibrium, invariant with evolutionary time horizon and robust to low extinction risks.

An Entropy-Based Approach to Portfolio Optimization.

This paper presents an improved method of applying entropy as a risk in portfolio optimization. A new family of portfolio optimization problems called the return-entropy portfolio optimization (REPO) is introduced that simplifies the computation of portfolio entropy using a combinatorial approach. REPO addresses five main practical concerns with the mean-variance portfolio optimization (MVPO). Pioneered by Harry Markowitz, MVPO revolutionized the financial industry as the first formal mathematical approach to risk-averse investing. REPO uses a mean-entropy objective function instead of the mean-variance objective function used in MVPO. REPO also simplifies the portfolio entropy calculation by utilizing combinatorial generating functions in the optimization objective function. REPO and MVPO were compared by emulating competing portfolios over historical data and REPO significantly outperformed MVPO in a strong majority of cases.

Spatial conservation planning under uncertainty: adapting to climate change risks using modern portfolio theory.

Climate change and urban growth impact habitats, species, and ecosystem services. To buffer against global change, an established adaptation strategy is designing protected areas to increase representation and complementarity of biodiversity features. Uncertainty regarding the scale and magnitude of landscape change complicates reserve planning and exposes decision makers to the risk of failing to meet conservation goals. Conservation planning tends to treat risk as an absolute measure, ignoring the context of the management problem and risk preferences of stakeholders. Application of risk management theory to conservation emphasizes the diversification of a portfolio of assets, with the goal of reducing the impact of system volatility on investment return. We use principles of Modern Portfolio Theory (MPT), which quantifies risk as the variance and correlation among assets, to formalize diversification as an explicit strategy for managing risk in climate-driven reserve design. We extend MPT to specify a framework that evaluates multiple conservation objectives, allows decision makers to balance management benefits and risk when preferences are contested or unknown, and includes additional decision options such as parcel divestment when evaluating candidate reserve designs. We apply an efficient search algorithm that optimizes portfolio design for large conservation problems and a game theoretic approach to evaluate portfolio trade-offs that satisfy decision makers with divergent benefit and risk tolerances, or when a single decision maker cannot resolve their own preferences. Evaluating several risk profiles for a case study in South Carolina, our results suggest that a reserve design may be somewhat robust to differences in risk attitude but that budgets will likely be important determinants of conservation planning strategies, particularly when divestment is considered a viable alternative. We identify a possible fiscal threshold where adequate resources allow protecting a sufficiently diverse portfolio of habitats such that the risk of failing to achieve conservation objectives is considerably lower. For a range of sea-level rise projections, conversion of habitat to open water (14-180%) and wetland loss (1-7%) are unable to be compensated under the current protected network. In contrast, optimal reserve design outcomes are predicted to ameliorate expected losses relative to current and future habitat protected under the existing conservation estate.

Utilising portfolio theory in environmental research - New perspectives and considerations.

Modern Portfolio Theory is a well-established method in economic research for considering the risks and returns in asset allocations and the potential benefits of diversification for risk averse agents. Thus, it is a useful tool for guiding sustainability discourse under uncertain future states. Existing discussions around the method's use in environmental research have evolved during over the 75 years of its application, leading to a continued renewal of perspectives on utilising it. We classify the environmental questions where portfolio theory has been applied, and critically discuss the methodological approaches taken; providing a stepping stone for future use of the method. This article provides a framework for its application in environmental research using the following questions: 1) what is the type of research or management question and objective(s) of the decision-maker(s); 2) what are the definitions of the assets to be included in the portfolio; 3) what are the ways that returns are valued, discounted, distributed and weighted; 4) what is the most appropriate way for risks to be accounted for and managed, including the selection of the appropriate model and taking into account risk preferences; and 5) what are the definitions of constraints in the programming problem.

Cover's universal portfolio, stochastic portfolio theory, and the numéraire portfolio.

Cover's celebrated theorem states that the long-run yield of a properly chosen "universal" portfolio is almost as good as that of the best retrospectively chosen constant rebalanced portfolio. The "universality" refers to the fact that this result is model-free, that is, not dependent on an underlying stochastic process. We extend Cover's theorem to the setting of stochastic portfolio theory: the market portfolio is taken as the numéraire, and the rebalancing rule need not be constant anymore but may depend on the current state of the stock market. By fixing a stochastic model of the stock market this model-free result is complemented by a comparison with the numéraire portfolio. Roughly speaking, under appropriate assumptions the asymptotic growth rate coincides for the three approaches mentioned in the title of this paper. We present results in both discrete and continuous time.

Integrating forest ecosystem services into the farming landscape: A stochastic economic assessment.

The objective of this study was to assess how payments for ecosystem services could assist plantation forestry's integration into pastoral dairy farming in order to improve environmental outcomes and increase business resilience to both price uncertainty and production limits imposed by environmental policies. Stochastic Dominance (SD) criteria and portfolio analysis, accounting for farmers' risk aversion levels, were used to rank different land-use alternatives and landscapes with different levels of plantation forestry integration. The study was focused on a modal 200-ha dairy farm in the Lake Rotorua Catchment of the Central North Island region of New Zealand, where national environmental policies are being implemented to improve water quality and reduce greenhouse gas emissions. Nitrogen and carbon payments would help farmers improve early cash flows for forestry, provide financial leverage to undertake afforestation projects and contribute to improved environmental outcomes for the catchment. The SD criteria demonstrated that although dairy farming generates the highest returns, plantation forestry with nitrogen and carbon payments would be a preferred alternative for landowners with relatively low risk aversion levels who consider return volatility and environmental limits within their land-use change criteria. Using the confidence premium concept, environmental payments to encourage plantation forestry into the landscape were shown to be lower when the majority of landowners are risk averse. The certainty equivalence approach helped to identify the optimal dairy-forestry portfolio arrangements for landowners of different levels of risk aversion, intensities of dairy farming (status quo and intensified) and nitrogen prices. At low nitrogen prices, risk neutral farmers would choose to afforest less than half of the farm and operate at the maximum nitrogen allowance, because dairy farming at both intensities provides the highest return among the different land uses available. However, at relatively low risk aversion levels, farmers would operate at levels below the maximum nitrogen allowance by including plantation forestry to a greater extent, compared to risk neutral farmers, due to its more certain returns. At a high nitrogen price of $400/kg, plantation forestry would completely subsume dairying, across risk aversion and intensity levels. These results confirm that plantation forestry as well as being an environmentally sound land-use alternative, also reduces uncertainty for landowners that are exposed to volatile international markets for dairy commodities.

Kidney transplantation outcomes: Is it possible to improve when good results are falling down?

Famure et al describe that close to 50% of their patients needed early or very early hospital readmissions after their kidney transplantation. As they taught us the variables related to those outcomes, we describe eight teaching capsules that may go beyond what they describe in their article. First two capsules talk about the ideal donors and recipients we should choose for avoiding the risk of an early readmission. The third and fourth capsules tell us about the reality of cadaveric donors and recipients with comorbidities, and the way transplant physicians should choose them to maximize survival. Fifth capsule shows that any mistake can result in an early readmission, and thus, in poorer outcomes. Sixth capsule talks about economic losses of early readmissions, cost-effectiveness of transplantation, and how to improve outcomes and reduce costs by managing a risky patient-portfolio. Seventh capsule argues about knowing your risk behavior to better manage your portfolio; and Eighth capsule about the importance of the center experience in transplanting complex patients. We finish with some lessons of the importance of the transplantation process and the collaboration with other disciplines in order to prevent the conditions that lead to early readmissions.

Application of Portfolio Theory to Healthcare Capacity Management.

Healthcare systems worldwide are faced with continuously increasing demand for care, while simultaneously experiencing insufficient capacity and unacceptably long patient waiting times. To improve healthcare access and availability, it is thus necessary to improve capacity utilization and increase the efficiency of existing resource usage. For this, variations in healthcare systems must be managed judiciously, and one solution is to apply a capacity pooling approach. A capacity pool is a general, collaborative capacity that can be allocated to parts of the system where the existing workload and demand for capacity are unusually high. In this study, we investigate how basic mean-variance methodology from portfolio theory can be applied as a capacity pooling approach to healthcare systems. A numerical example based on fictitious data is used to illustrate the theoretical value of using a portfolio approach in a capacity pooling context. The example shows that there are opportunities to use capacity more efficiently and increase service levels, given the same capacity, and that a mean-variance analysis could be performed to theoretically dimension the most efficient pooling organization. The study concludes with a discussion regarding the practical usefulness of this methodology in the healthcare context.

Mixed-stock analysis using Rapture genotyping to evaluate stock-specific exploitation of a walleye population despite weak genetic structure.

Mixed-stock analyses using genetic markers have informed fisheries management in cases where strong genetic differentiation occurs among local spawning populations, yet many fisheries are supported by multiple, weakly differentiated stocks. Freshwater fisheries exemplify this problem, with many populations supported by multiple stocks of young evolutionary age and isolated across small spatial scales. Consequently, attempts to conduct genetic mixed-stock analyses of inland fisheries have often been unsuccessful. Advances in genomic sequencing offer the ability to discriminate among populations with weak population structure, providing the necessary resolution to conduct mixed-stock assignment among previously indistinguishable stocks. We used genomic data to conduct a mixed-stock analysis of eastern Lake Erie's commercial and recreational walleye (Sander vitreus) fisheries and estimate the relative harvest of weakly differentiated stocks (pairwise F ST < 0.01). Using RAD-capture (Rapture), we sequenced and genotyped individuals from western and eastern basin local spawning stocks at 12,081 loci with 95% reassignment accuracy, which was not possible in the past using microsatellite markers. A baseline assessment of 395 walleye from 11 spawning stocks identified three reporting groups and refined previous assessments of gene flow among walleye stocks. Genetic assignment of 1,075 walleye harvested in eastern Lake Erie's recreational and commercial fisheries indicated that western basin stocks constituted the majority of harvest during the peak walleye fishing season (July-September), whereas eastern basin individuals comprised much of the early season harvest (May-June). Clear spatial structure in harvest composition existed; catches in more easterly sites contained more individuals of eastern basin origin than did more westerly sites. Our study provides important stock contribution estimates for Lake Erie fishery management and demonstrates the utility of genomic data to facilitate mixed-stock analysis in exploited fish populations having weak population structure or limited existing genetic resources.

Financial portfolio approach to optimal tourist market mixes.

This study applied a financial portfolio theory to estimate optimal market mixes to minimize the instability of inbound tourist market demand. An empirical analysis was applied to inbound tourists to Taiwan. The results shed light on diversification in tourism market and offer tourism authorities and policy-makers explicit guidelines for risk management in the destination planning process. Specifically, using optimal mixes with various return/risk options can facilitate a more stable pattern of arrivals from foreign countries. To achieve the Doubling Tourist Arrivals Plan, introduced by the Taiwanese government in 2002, the tourism authorities should take the high-return/high-risk option and shift available resources to Japan. More policy implications are provided to guide tourism authorities and policy makers.